Actual source code: theta.c
1: /*
2: Code for timestepping with implicit Theta method
3: */
4: #include <petsc/private/tsimpl.h>
5: #include <petscsnes.h>
6: #include <petscdm.h>
7: #include <petscmat.h>
9: typedef struct {
10: /* context for time stepping */
11: PetscReal stage_time;
12: Vec Stages[2]; /* Storage for stage solutions */
13: Vec X0, X, Xdot; /* Storage for u^n, u^n + dt a_{11} k_1, and time derivative u^{n+1}_t */
14: Vec affine; /* Affine vector needed for residual at beginning of step in endpoint formulation */
15: PetscReal Theta;
16: PetscReal shift; /* Shift parameter for SNES Jacobian, used by forward, TLM and adjoint */
17: PetscInt order;
18: PetscBool endpoint;
19: PetscBool extrapolate;
20: TSStepStatus status;
21: Vec VecCostIntegral0; /* Backup for roll-backs due to events, used by cost integral */
22: PetscReal ptime0; /* Backup for ts->ptime, the start time of current time step, used by TLM and cost integral */
23: PetscReal time_step0; /* Backup for ts->timestep, the step size of current time step, used by TLM and cost integral*/
25: /* context for sensitivity analysis */
26: PetscInt num_tlm; /* Total number of tangent linear equations */
27: Vec *VecsDeltaLam; /* Increment of the adjoint sensitivity w.r.t IC at stage */
28: Vec *VecsDeltaMu; /* Increment of the adjoint sensitivity w.r.t P at stage */
29: Vec *VecsSensiTemp; /* Vector to be multiplied with Jacobian transpose */
30: Mat MatFwdStages[2]; /* TLM Stages */
31: Mat MatDeltaFwdSensip; /* Increment of the forward sensitivity at stage */
32: Vec VecDeltaFwdSensipCol; /* Working vector for holding one column of the sensitivity matrix */
33: Mat MatFwdSensip0; /* backup for roll-backs due to events */
34: Mat MatIntegralSensipTemp; /* Working vector for forward integral sensitivity */
35: Mat MatIntegralSensip0; /* backup for roll-backs due to events */
36: Vec *VecsDeltaLam2; /* Increment of the 2nd-order adjoint sensitivity w.r.t IC at stage */
37: Vec *VecsDeltaMu2; /* Increment of the 2nd-order adjoint sensitivity w.r.t P at stage */
38: Vec *VecsSensi2Temp; /* Working vectors that holds the residual for the second-order adjoint */
39: Vec *VecsAffine; /* Working vectors to store residuals */
40: /* context for error estimation */
41: Vec vec_sol_prev;
42: Vec vec_lte_work;
43: } TS_Theta;
45: static PetscErrorCode TSThetaGetX0AndXdot(TS ts, DM dm, Vec *X0, Vec *Xdot)
46: {
47: TS_Theta *th = (TS_Theta *)ts->data;
49: PetscFunctionBegin;
50: if (X0) {
51: if (dm && dm != ts->dm) PetscCall(DMGetNamedGlobalVector(dm, "TSTheta_X0", X0));
52: else *X0 = ts->vec_sol;
53: }
54: if (Xdot) {
55: if (dm && dm != ts->dm) PetscCall(DMGetNamedGlobalVector(dm, "TSTheta_Xdot", Xdot));
56: else *Xdot = th->Xdot;
57: }
58: PetscFunctionReturn(PETSC_SUCCESS);
59: }
61: static PetscErrorCode TSThetaRestoreX0AndXdot(TS ts, DM dm, Vec *X0, Vec *Xdot)
62: {
63: PetscFunctionBegin;
64: if (X0) {
65: if (dm && dm != ts->dm) PetscCall(DMRestoreNamedGlobalVector(dm, "TSTheta_X0", X0));
66: }
67: if (Xdot) {
68: if (dm && dm != ts->dm) PetscCall(DMRestoreNamedGlobalVector(dm, "TSTheta_Xdot", Xdot));
69: }
70: PetscFunctionReturn(PETSC_SUCCESS);
71: }
73: static PetscErrorCode DMCoarsenHook_TSTheta(DM fine, DM coarse, PetscCtx ctx)
74: {
75: PetscFunctionBegin;
76: PetscFunctionReturn(PETSC_SUCCESS);
77: }
79: static PetscErrorCode DMRestrictHook_TSTheta(DM fine, Mat restrct, Vec rscale, Mat inject, DM coarse, PetscCtx ctx)
80: {
81: TS ts = (TS)ctx;
82: Vec X0, Xdot, X0_c, Xdot_c;
84: PetscFunctionBegin;
85: PetscCall(TSThetaGetX0AndXdot(ts, fine, &X0, &Xdot));
86: PetscCall(TSThetaGetX0AndXdot(ts, coarse, &X0_c, &Xdot_c));
87: PetscCall(MatRestrict(restrct, X0, X0_c));
88: PetscCall(MatRestrict(restrct, Xdot, Xdot_c));
89: PetscCall(VecPointwiseMult(X0_c, rscale, X0_c));
90: PetscCall(VecPointwiseMult(Xdot_c, rscale, Xdot_c));
91: PetscCall(TSThetaRestoreX0AndXdot(ts, fine, &X0, &Xdot));
92: PetscCall(TSThetaRestoreX0AndXdot(ts, coarse, &X0_c, &Xdot_c));
93: PetscFunctionReturn(PETSC_SUCCESS);
94: }
96: static PetscErrorCode DMSubDomainHook_TSTheta(DM dm, DM subdm, PetscCtx ctx)
97: {
98: PetscFunctionBegin;
99: PetscFunctionReturn(PETSC_SUCCESS);
100: }
102: static PetscErrorCode DMSubDomainRestrictHook_TSTheta(DM dm, VecScatter gscat, VecScatter lscat, DM subdm, PetscCtx ctx)
103: {
104: TS ts = (TS)ctx;
105: Vec X0, Xdot, X0_sub, Xdot_sub;
107: PetscFunctionBegin;
108: PetscCall(TSThetaGetX0AndXdot(ts, dm, &X0, &Xdot));
109: PetscCall(TSThetaGetX0AndXdot(ts, subdm, &X0_sub, &Xdot_sub));
111: PetscCall(VecScatterBegin(gscat, X0, X0_sub, INSERT_VALUES, SCATTER_FORWARD));
112: PetscCall(VecScatterEnd(gscat, X0, X0_sub, INSERT_VALUES, SCATTER_FORWARD));
114: PetscCall(VecScatterBegin(gscat, Xdot, Xdot_sub, INSERT_VALUES, SCATTER_FORWARD));
115: PetscCall(VecScatterEnd(gscat, Xdot, Xdot_sub, INSERT_VALUES, SCATTER_FORWARD));
117: PetscCall(TSThetaRestoreX0AndXdot(ts, dm, &X0, &Xdot));
118: PetscCall(TSThetaRestoreX0AndXdot(ts, subdm, &X0_sub, &Xdot_sub));
119: PetscFunctionReturn(PETSC_SUCCESS);
120: }
122: static PetscErrorCode TSThetaEvaluateCostIntegral(TS ts)
123: {
124: TS_Theta *th = (TS_Theta *)ts->data;
125: TS quadts = ts->quadraturets;
127: PetscFunctionBegin;
128: if (th->endpoint) {
129: /* Evolve ts->vec_costintegral to compute integrals */
130: if (th->Theta != 1.0) {
131: PetscCall(TSComputeRHSFunction(quadts, th->ptime0, th->X0, ts->vec_costintegrand));
132: PetscCall(VecAXPY(quadts->vec_sol, th->time_step0 * (1.0 - th->Theta), ts->vec_costintegrand));
133: }
134: PetscCall(TSComputeRHSFunction(quadts, ts->ptime, ts->vec_sol, ts->vec_costintegrand));
135: PetscCall(VecAXPY(quadts->vec_sol, th->time_step0 * th->Theta, ts->vec_costintegrand));
136: } else {
137: PetscCall(TSComputeRHSFunction(quadts, th->stage_time, th->X, ts->vec_costintegrand));
138: PetscCall(VecAXPY(quadts->vec_sol, th->time_step0, ts->vec_costintegrand));
139: }
140: PetscFunctionReturn(PETSC_SUCCESS);
141: }
143: static PetscErrorCode TSForwardCostIntegral_Theta(TS ts)
144: {
145: TS_Theta *th = (TS_Theta *)ts->data;
146: TS quadts = ts->quadraturets;
148: PetscFunctionBegin;
149: /* backup cost integral */
150: PetscCall(VecCopy(quadts->vec_sol, th->VecCostIntegral0));
151: PetscCall(TSThetaEvaluateCostIntegral(ts));
152: PetscFunctionReturn(PETSC_SUCCESS);
153: }
155: static PetscErrorCode TSAdjointCostIntegral_Theta(TS ts)
156: {
157: TS_Theta *th = (TS_Theta *)ts->data;
159: PetscFunctionBegin;
160: /* Like TSForwardCostIntegral(), the adjoint cost integral evaluation relies on ptime0 and time_step0. */
161: th->ptime0 = ts->ptime + ts->time_step;
162: th->time_step0 = -ts->time_step;
163: PetscCall(TSThetaEvaluateCostIntegral(ts));
164: PetscFunctionReturn(PETSC_SUCCESS);
165: }
167: static PetscErrorCode TSTheta_SNESSolve(TS ts, Vec b, Vec x)
168: {
169: PetscInt nits, lits;
171: PetscFunctionBegin;
172: PetscCall(SNESSolve(ts->snes, b, x));
173: PetscCall(SNESGetIterationNumber(ts->snes, &nits));
174: PetscCall(SNESGetLinearSolveIterations(ts->snes, &lits));
175: ts->snes_its += nits;
176: ts->ksp_its += lits;
177: PetscFunctionReturn(PETSC_SUCCESS);
178: }
180: static PetscErrorCode TSResizeRegister_Theta(TS ts, PetscBool reg)
181: {
182: TS_Theta *th = (TS_Theta *)ts->data;
184: PetscFunctionBegin;
185: if (reg) {
186: PetscCall(TSResizeRegisterVec(ts, "ts:theta:sol_prev", th->vec_sol_prev));
187: PetscCall(TSResizeRegisterVec(ts, "ts:theta:X0", th->X0));
188: } else {
189: PetscCall(TSResizeRetrieveVec(ts, "ts:theta:sol_prev", &th->vec_sol_prev));
190: PetscCall(PetscObjectReference((PetscObject)th->vec_sol_prev));
191: PetscCall(TSResizeRetrieveVec(ts, "ts:theta:X0", &th->X0));
192: PetscCall(PetscObjectReference((PetscObject)th->X0));
193: }
194: PetscFunctionReturn(PETSC_SUCCESS);
195: }
197: static PetscErrorCode TSStep_Theta(TS ts)
198: {
199: TS_Theta *th = (TS_Theta *)ts->data;
200: PetscInt rejections = 0;
201: PetscBool stageok, accept = PETSC_TRUE;
202: PetscReal next_time_step = ts->time_step;
204: PetscFunctionBegin;
205: if (!ts->steprollback) {
206: if (th->vec_sol_prev) PetscCall(VecCopy(th->X0, th->vec_sol_prev));
207: PetscCall(VecCopy(ts->vec_sol, th->X0));
208: }
210: th->status = TS_STEP_INCOMPLETE;
211: while (!ts->reason && th->status != TS_STEP_COMPLETE) {
212: th->shift = 1 / (th->Theta * ts->time_step);
213: th->stage_time = ts->ptime + (th->endpoint ? (PetscReal)1 : th->Theta) * ts->time_step;
214: PetscCall(VecCopy(th->X0, th->X));
215: if (th->extrapolate && !ts->steprestart) PetscCall(VecAXPY(th->X, 1 / th->shift, th->Xdot));
216: if (th->endpoint) { /* This formulation assumes linear time-independent mass matrix */
217: if (!th->affine) PetscCall(VecDuplicate(ts->vec_sol, &th->affine));
218: PetscCall(VecZeroEntries(th->Xdot));
219: PetscCall(TSComputeIFunction(ts, ts->ptime, th->X0, th->Xdot, th->affine, PETSC_FALSE));
220: PetscCall(VecScale(th->affine, (th->Theta - 1) / th->Theta));
221: }
222: PetscCall(TSPreStage(ts, th->stage_time));
223: PetscCall(TSTheta_SNESSolve(ts, th->endpoint ? th->affine : NULL, th->X));
224: PetscCall(TSPostStage(ts, th->stage_time, 0, &th->X));
225: PetscCall(TSAdaptCheckStage(ts->adapt, ts, th->stage_time, th->X, &stageok));
226: if (!stageok) goto reject_step;
228: if (th->endpoint) {
229: PetscCall(VecCopy(th->X, ts->vec_sol));
230: } else {
231: PetscCall(VecAXPBYPCZ(th->Xdot, -th->shift, th->shift, 0, th->X0, th->X)); /* th->Xdot is needed by TSInterpolate_Theta */
232: if (th->Theta == 1.0) PetscCall(VecCopy(th->X, ts->vec_sol)); /* BEULER, stage already checked */
233: else {
234: PetscCall(VecAXPY(ts->vec_sol, ts->time_step, th->Xdot));
235: PetscCall(TSAdaptCheckStage(ts->adapt, ts, ts->ptime + ts->time_step, ts->vec_sol, &stageok));
236: if (!stageok) {
237: PetscCall(VecCopy(th->X0, ts->vec_sol));
238: goto reject_step;
239: }
240: }
241: }
243: th->status = TS_STEP_PENDING;
244: PetscCall(TSAdaptChoose(ts->adapt, ts, ts->time_step, NULL, &next_time_step, &accept));
245: th->status = accept ? TS_STEP_COMPLETE : TS_STEP_INCOMPLETE;
246: if (!accept) {
247: PetscCall(VecCopy(th->X0, ts->vec_sol));
248: ts->time_step = next_time_step;
249: goto reject_step;
250: }
252: if (ts->forward_solve || ts->costintegralfwd) { /* Save the info for the later use in cost integral evaluation */
253: th->ptime0 = ts->ptime;
254: th->time_step0 = ts->time_step;
255: }
256: ts->ptime += ts->time_step;
257: ts->time_step = next_time_step;
258: break;
260: reject_step:
261: ts->reject++;
262: accept = PETSC_FALSE;
263: if (!ts->reason && ++rejections > ts->max_reject && ts->max_reject >= 0) {
264: ts->reason = TS_DIVERGED_STEP_REJECTED;
265: PetscCall(PetscInfo(ts, "Step=%" PetscInt_FMT ", step rejections %" PetscInt_FMT " greater than current TS allowed, stopping solve\n", ts->steps, rejections));
266: }
267: }
268: PetscFunctionReturn(PETSC_SUCCESS);
269: }
271: static PetscErrorCode TSAdjointStepBEuler_Private(TS ts)
272: {
273: TS_Theta *th = (TS_Theta *)ts->data;
274: TS quadts = ts->quadraturets;
275: Vec *VecsDeltaLam = th->VecsDeltaLam, *VecsDeltaMu = th->VecsDeltaMu, *VecsSensiTemp = th->VecsSensiTemp;
276: Vec *VecsDeltaLam2 = th->VecsDeltaLam2, *VecsDeltaMu2 = th->VecsDeltaMu2, *VecsSensi2Temp = th->VecsSensi2Temp;
277: PetscInt nadj;
278: Mat J, Jpre, quadJ = NULL, quadJp = NULL;
279: KSP ksp;
280: PetscScalar *xarr;
281: TSEquationType eqtype;
282: PetscBool isexplicitode = PETSC_FALSE;
283: PetscReal adjoint_time_step;
285: PetscFunctionBegin;
286: PetscCall(TSGetEquationType(ts, &eqtype));
287: if (eqtype == TS_EQ_ODE_EXPLICIT) {
288: isexplicitode = PETSC_TRUE;
289: VecsDeltaLam = ts->vecs_sensi;
290: VecsDeltaLam2 = ts->vecs_sensi2;
291: }
292: th->status = TS_STEP_INCOMPLETE;
293: PetscCall(SNESGetKSP(ts->snes, &ksp));
294: PetscCall(TSGetIJacobian(ts, &J, &Jpre, NULL, NULL));
295: if (quadts) {
296: PetscCall(TSGetRHSJacobian(quadts, &quadJ, NULL, NULL, NULL));
297: PetscCall(TSGetRHSJacobianP(quadts, &quadJp, NULL, NULL));
298: }
300: th->stage_time = ts->ptime;
301: adjoint_time_step = -ts->time_step; /* always positive since time_step is negative */
303: /* Build RHS for first-order adjoint lambda_{n+1}/h + r_u^T(n+1) */
304: if (quadts) PetscCall(TSComputeRHSJacobian(quadts, th->stage_time, ts->vec_sol, quadJ, NULL));
306: for (nadj = 0; nadj < ts->numcost; nadj++) {
307: PetscCall(VecCopy(ts->vecs_sensi[nadj], VecsSensiTemp[nadj]));
308: PetscCall(VecScale(VecsSensiTemp[nadj], 1. / adjoint_time_step)); /* lambda_{n+1}/h */
309: if (quadJ) {
310: PetscCall(MatDenseGetColumn(quadJ, nadj, &xarr));
311: PetscCall(VecPlaceArray(ts->vec_drdu_col, xarr));
312: PetscCall(VecAXPY(VecsSensiTemp[nadj], 1., ts->vec_drdu_col));
313: PetscCall(VecResetArray(ts->vec_drdu_col));
314: PetscCall(MatDenseRestoreColumn(quadJ, &xarr));
315: }
316: }
318: /* Build LHS for first-order adjoint */
319: th->shift = 1. / adjoint_time_step;
320: PetscCall(TSComputeSNESJacobian(ts, ts->vec_sol, J, Jpre));
321: PetscCall(KSPSetOperators(ksp, J, Jpre));
323: /* Solve stage equation LHS*lambda_s = RHS for first-order adjoint */
324: for (nadj = 0; nadj < ts->numcost; nadj++) {
325: KSPConvergedReason kspreason;
326: PetscCall(KSPSolveTranspose(ksp, VecsSensiTemp[nadj], VecsDeltaLam[nadj]));
327: PetscCall(KSPGetConvergedReason(ksp, &kspreason));
328: if (kspreason < 0) {
329: ts->reason = TSADJOINT_DIVERGED_LINEAR_SOLVE;
330: PetscCall(PetscInfo(ts, "Step=%" PetscInt_FMT ", %" PetscInt_FMT "th cost function, transposed linear solve fails, stopping 1st-order adjoint solve\n", ts->steps, nadj));
331: }
332: }
334: if (ts->vecs_sensi2) { /* U_{n+1} */
335: /* Get w1 at t_{n+1} from TLM matrix */
336: PetscCall(MatDenseGetColumn(ts->mat_sensip, 0, &xarr));
337: PetscCall(VecPlaceArray(ts->vec_sensip_col, xarr));
338: /* lambda_s^T F_UU w_1 */
339: PetscCall(TSComputeIHessianProductFunctionUU(ts, th->stage_time, ts->vec_sol, VecsDeltaLam, ts->vec_sensip_col, ts->vecs_fuu));
340: /* lambda_s^T F_UP w_2 */
341: PetscCall(TSComputeIHessianProductFunctionUP(ts, th->stage_time, ts->vec_sol, VecsDeltaLam, ts->vec_dir, ts->vecs_fup));
342: for (nadj = 0; nadj < ts->numcost; nadj++) { /* compute the residual */
343: PetscCall(VecCopy(ts->vecs_sensi2[nadj], VecsSensi2Temp[nadj]));
344: PetscCall(VecScale(VecsSensi2Temp[nadj], 1. / adjoint_time_step));
345: PetscCall(VecAXPY(VecsSensi2Temp[nadj], -1., ts->vecs_fuu[nadj]));
346: if (ts->vecs_fup) PetscCall(VecAXPY(VecsSensi2Temp[nadj], -1., ts->vecs_fup[nadj]));
347: }
348: /* Solve stage equation LHS X = RHS for second-order adjoint */
349: for (nadj = 0; nadj < ts->numcost; nadj++) {
350: KSPConvergedReason kspreason;
351: PetscCall(KSPSolveTranspose(ksp, VecsSensi2Temp[nadj], VecsDeltaLam2[nadj]));
352: PetscCall(KSPGetConvergedReason(ksp, &kspreason));
353: if (kspreason < 0) {
354: ts->reason = TSADJOINT_DIVERGED_LINEAR_SOLVE;
355: PetscCall(PetscInfo(ts, "Step=%" PetscInt_FMT ", %" PetscInt_FMT "th cost function, transposed linear solve fails, stopping 2nd-order adjoint solve\n", ts->steps, nadj));
356: }
357: }
358: }
360: /* Update sensitivities, and evaluate integrals if there is any */
361: if (!isexplicitode) {
362: th->shift = 0.0;
363: PetscCall(TSComputeSNESJacobian(ts, ts->vec_sol, J, Jpre));
364: PetscCall(KSPSetOperators(ksp, J, Jpre));
365: for (nadj = 0; nadj < ts->numcost; nadj++) {
366: /* Add f_U \lambda_s to the original RHS */
367: PetscCall(VecScale(VecsSensiTemp[nadj], -1.));
368: PetscCall(MatMultTransposeAdd(J, VecsDeltaLam[nadj], VecsSensiTemp[nadj], VecsSensiTemp[nadj]));
369: PetscCall(VecScale(VecsSensiTemp[nadj], -adjoint_time_step));
370: PetscCall(VecCopy(VecsSensiTemp[nadj], ts->vecs_sensi[nadj]));
371: if (ts->vecs_sensi2) {
372: PetscCall(MatMultTransposeAdd(J, VecsDeltaLam2[nadj], VecsSensi2Temp[nadj], VecsSensi2Temp[nadj]));
373: PetscCall(VecScale(VecsSensi2Temp[nadj], -adjoint_time_step));
374: PetscCall(VecCopy(VecsSensi2Temp[nadj], ts->vecs_sensi2[nadj]));
375: }
376: }
377: }
378: if (ts->vecs_sensip) {
379: PetscCall(VecAXPBYPCZ(th->Xdot, -1. / adjoint_time_step, 1.0 / adjoint_time_step, 0, th->X0, ts->vec_sol));
380: PetscCall(TSComputeIJacobianP(ts, th->stage_time, ts->vec_sol, th->Xdot, 1. / adjoint_time_step, ts->Jacp, PETSC_FALSE)); /* get -f_p */
381: if (quadts) PetscCall(TSComputeRHSJacobianP(quadts, th->stage_time, ts->vec_sol, quadJp));
382: if (ts->vecs_sensi2p) {
383: /* lambda_s^T F_PU w_1 */
384: PetscCall(TSComputeIHessianProductFunctionPU(ts, th->stage_time, ts->vec_sol, VecsDeltaLam, ts->vec_sensip_col, ts->vecs_fpu));
385: /* lambda_s^T F_PP w_2 */
386: PetscCall(TSComputeIHessianProductFunctionPP(ts, th->stage_time, ts->vec_sol, VecsDeltaLam, ts->vec_dir, ts->vecs_fpp));
387: }
389: for (nadj = 0; nadj < ts->numcost; nadj++) {
390: PetscCall(MatMultTranspose(ts->Jacp, VecsDeltaLam[nadj], VecsDeltaMu[nadj]));
391: PetscCall(VecAXPY(ts->vecs_sensip[nadj], -adjoint_time_step, VecsDeltaMu[nadj]));
392: if (quadJp) {
393: PetscCall(MatDenseGetColumn(quadJp, nadj, &xarr));
394: PetscCall(VecPlaceArray(ts->vec_drdp_col, xarr));
395: PetscCall(VecAXPY(ts->vecs_sensip[nadj], adjoint_time_step, ts->vec_drdp_col));
396: PetscCall(VecResetArray(ts->vec_drdp_col));
397: PetscCall(MatDenseRestoreColumn(quadJp, &xarr));
398: }
399: if (ts->vecs_sensi2p) {
400: PetscCall(MatMultTranspose(ts->Jacp, VecsDeltaLam2[nadj], VecsDeltaMu2[nadj]));
401: PetscCall(VecAXPY(ts->vecs_sensi2p[nadj], -adjoint_time_step, VecsDeltaMu2[nadj]));
402: if (ts->vecs_fpu) PetscCall(VecAXPY(ts->vecs_sensi2p[nadj], -adjoint_time_step, ts->vecs_fpu[nadj]));
403: if (ts->vecs_fpp) PetscCall(VecAXPY(ts->vecs_sensi2p[nadj], -adjoint_time_step, ts->vecs_fpp[nadj]));
404: }
405: }
406: }
408: if (ts->vecs_sensi2) {
409: PetscCall(VecResetArray(ts->vec_sensip_col));
410: PetscCall(MatDenseRestoreColumn(ts->mat_sensip, &xarr));
411: }
412: th->status = TS_STEP_COMPLETE;
413: PetscFunctionReturn(PETSC_SUCCESS);
414: }
416: static PetscErrorCode TSAdjointStep_Theta(TS ts)
417: {
418: TS_Theta *th = (TS_Theta *)ts->data;
419: TS quadts = ts->quadraturets;
420: Vec *VecsDeltaLam = th->VecsDeltaLam, *VecsDeltaMu = th->VecsDeltaMu, *VecsSensiTemp = th->VecsSensiTemp;
421: Vec *VecsDeltaLam2 = th->VecsDeltaLam2, *VecsDeltaMu2 = th->VecsDeltaMu2, *VecsSensi2Temp = th->VecsSensi2Temp;
422: PetscInt nadj;
423: Mat J, Jpre, quadJ = NULL, quadJp = NULL;
424: KSP ksp;
425: PetscScalar *xarr;
426: PetscReal adjoint_time_step;
427: PetscReal adjoint_ptime; /* end time of the adjoint time step (ts->ptime is the start time, usually ts->ptime is larger than adjoint_ptime) */
429: PetscFunctionBegin;
430: if (th->Theta == 1.) {
431: PetscCall(TSAdjointStepBEuler_Private(ts));
432: PetscFunctionReturn(PETSC_SUCCESS);
433: }
434: th->status = TS_STEP_INCOMPLETE;
435: PetscCall(SNESGetKSP(ts->snes, &ksp));
436: PetscCall(TSGetIJacobian(ts, &J, &Jpre, NULL, NULL));
437: if (quadts) {
438: PetscCall(TSGetRHSJacobian(quadts, &quadJ, NULL, NULL, NULL));
439: PetscCall(TSGetRHSJacobianP(quadts, &quadJp, NULL, NULL));
440: }
441: /* If endpoint=1, th->ptime and th->X0 will be used; if endpoint=0, th->stage_time and th->X will be used. */
442: th->stage_time = th->endpoint ? ts->ptime : (ts->ptime + (1. - th->Theta) * ts->time_step);
443: adjoint_ptime = ts->ptime + ts->time_step;
444: adjoint_time_step = -ts->time_step; /* always positive since time_step is negative */
446: if (!th->endpoint) {
447: /* recover th->X0 using vec_sol and the stage value th->X */
448: PetscCall(VecAXPBYPCZ(th->X0, 1.0 / (1.0 - th->Theta), th->Theta / (th->Theta - 1.0), 0, th->X, ts->vec_sol));
449: }
451: /* Build RHS for first-order adjoint */
452: /* Cost function has an integral term */
453: if (quadts) {
454: if (th->endpoint) {
455: PetscCall(TSComputeRHSJacobian(quadts, th->stage_time, ts->vec_sol, quadJ, NULL));
456: } else {
457: PetscCall(TSComputeRHSJacobian(quadts, th->stage_time, th->X, quadJ, NULL));
458: }
459: }
461: for (nadj = 0; nadj < ts->numcost; nadj++) {
462: PetscCall(VecCopy(ts->vecs_sensi[nadj], VecsSensiTemp[nadj]));
463: PetscCall(VecScale(VecsSensiTemp[nadj], 1. / (th->Theta * adjoint_time_step)));
464: if (quadJ) {
465: PetscCall(MatDenseGetColumn(quadJ, nadj, &xarr));
466: PetscCall(VecPlaceArray(ts->vec_drdu_col, xarr));
467: PetscCall(VecAXPY(VecsSensiTemp[nadj], 1., ts->vec_drdu_col));
468: PetscCall(VecResetArray(ts->vec_drdu_col));
469: PetscCall(MatDenseRestoreColumn(quadJ, &xarr));
470: }
471: }
473: /* Build LHS for first-order adjoint */
474: th->shift = 1. / (th->Theta * adjoint_time_step);
475: if (th->endpoint) {
476: PetscCall(TSComputeSNESJacobian(ts, ts->vec_sol, J, Jpre));
477: } else {
478: PetscCall(TSComputeSNESJacobian(ts, th->X, J, Jpre));
479: }
480: PetscCall(KSPSetOperators(ksp, J, Jpre));
482: /* Solve stage equation LHS*lambda_s = RHS for first-order adjoint */
483: for (nadj = 0; nadj < ts->numcost; nadj++) {
484: KSPConvergedReason kspreason;
485: PetscCall(KSPSolveTranspose(ksp, VecsSensiTemp[nadj], VecsDeltaLam[nadj]));
486: PetscCall(KSPGetConvergedReason(ksp, &kspreason));
487: if (kspreason < 0) {
488: ts->reason = TSADJOINT_DIVERGED_LINEAR_SOLVE;
489: PetscCall(PetscInfo(ts, "Step=%" PetscInt_FMT ", %" PetscInt_FMT "th cost function, transposed linear solve fails, stopping 1st-order adjoint solve\n", ts->steps, nadj));
490: }
491: }
493: /* Second-order adjoint */
494: if (ts->vecs_sensi2) { /* U_{n+1} */
495: PetscCheck(th->endpoint, PetscObjectComm((PetscObject)ts), PETSC_ERR_SUP, "Operation not implemented in TS_Theta");
496: /* Get w1 at t_{n+1} from TLM matrix */
497: PetscCall(MatDenseGetColumn(ts->mat_sensip, 0, &xarr));
498: PetscCall(VecPlaceArray(ts->vec_sensip_col, xarr));
499: /* lambda_s^T F_UU w_1 */
500: PetscCall(TSComputeIHessianProductFunctionUU(ts, th->stage_time, ts->vec_sol, VecsDeltaLam, ts->vec_sensip_col, ts->vecs_fuu));
501: PetscCall(VecResetArray(ts->vec_sensip_col));
502: PetscCall(MatDenseRestoreColumn(ts->mat_sensip, &xarr));
503: /* lambda_s^T F_UP w_2 */
504: PetscCall(TSComputeIHessianProductFunctionUP(ts, th->stage_time, ts->vec_sol, VecsDeltaLam, ts->vec_dir, ts->vecs_fup));
505: for (nadj = 0; nadj < ts->numcost; nadj++) { /* compute the residual */
506: PetscCall(VecCopy(ts->vecs_sensi2[nadj], VecsSensi2Temp[nadj]));
507: PetscCall(VecScale(VecsSensi2Temp[nadj], th->shift));
508: PetscCall(VecAXPY(VecsSensi2Temp[nadj], -1., ts->vecs_fuu[nadj]));
509: if (ts->vecs_fup) PetscCall(VecAXPY(VecsSensi2Temp[nadj], -1., ts->vecs_fup[nadj]));
510: }
511: /* Solve stage equation LHS X = RHS for second-order adjoint */
512: for (nadj = 0; nadj < ts->numcost; nadj++) {
513: KSPConvergedReason kspreason;
514: PetscCall(KSPSolveTranspose(ksp, VecsSensi2Temp[nadj], VecsDeltaLam2[nadj]));
515: PetscCall(KSPGetConvergedReason(ksp, &kspreason));
516: if (kspreason < 0) {
517: ts->reason = TSADJOINT_DIVERGED_LINEAR_SOLVE;
518: PetscCall(PetscInfo(ts, "Step=%" PetscInt_FMT ", %" PetscInt_FMT "th cost function, transposed linear solve fails, stopping 2nd-order adjoint solve\n", ts->steps, nadj));
519: }
520: }
521: }
523: /* Update sensitivities, and evaluate integrals if there is any */
524: if (th->endpoint) { /* two-stage Theta methods with th->Theta!=1, th->Theta==1 leads to BEuler */
525: th->shift = 1. / ((th->Theta - 1.) * adjoint_time_step);
526: th->stage_time = adjoint_ptime;
527: PetscCall(TSComputeSNESJacobian(ts, th->X0, J, Jpre));
528: PetscCall(KSPSetOperators(ksp, J, Jpre));
529: /* R_U at t_n */
530: if (quadts) PetscCall(TSComputeRHSJacobian(quadts, adjoint_ptime, th->X0, quadJ, NULL));
531: for (nadj = 0; nadj < ts->numcost; nadj++) {
532: PetscCall(MatMultTranspose(J, VecsDeltaLam[nadj], ts->vecs_sensi[nadj]));
533: if (quadJ) {
534: PetscCall(MatDenseGetColumn(quadJ, nadj, &xarr));
535: PetscCall(VecPlaceArray(ts->vec_drdu_col, xarr));
536: PetscCall(VecAXPY(ts->vecs_sensi[nadj], -1., ts->vec_drdu_col));
537: PetscCall(VecResetArray(ts->vec_drdu_col));
538: PetscCall(MatDenseRestoreColumn(quadJ, &xarr));
539: }
540: PetscCall(VecScale(ts->vecs_sensi[nadj], 1. / th->shift));
541: }
543: /* Second-order adjoint */
544: if (ts->vecs_sensi2) { /* U_n */
545: /* Get w1 at t_n from TLM matrix */
546: PetscCall(MatDenseGetColumn(th->MatFwdSensip0, 0, &xarr));
547: PetscCall(VecPlaceArray(ts->vec_sensip_col, xarr));
548: /* lambda_s^T F_UU w_1 */
549: PetscCall(TSComputeIHessianProductFunctionUU(ts, adjoint_ptime, th->X0, VecsDeltaLam, ts->vec_sensip_col, ts->vecs_fuu));
550: PetscCall(VecResetArray(ts->vec_sensip_col));
551: PetscCall(MatDenseRestoreColumn(th->MatFwdSensip0, &xarr));
552: /* lambda_s^T F_UU w_2 */
553: PetscCall(TSComputeIHessianProductFunctionUP(ts, adjoint_ptime, th->X0, VecsDeltaLam, ts->vec_dir, ts->vecs_fup));
554: for (nadj = 0; nadj < ts->numcost; nadj++) {
555: /* M^T Lambda_s + h(1-theta) F_U^T Lambda_s + h(1-theta) lambda_s^T F_UU w_1 + lambda_s^T F_UP w_2 */
556: PetscCall(MatMultTranspose(J, VecsDeltaLam2[nadj], ts->vecs_sensi2[nadj]));
557: PetscCall(VecAXPY(ts->vecs_sensi2[nadj], 1., ts->vecs_fuu[nadj]));
558: if (ts->vecs_fup) PetscCall(VecAXPY(ts->vecs_sensi2[nadj], 1., ts->vecs_fup[nadj]));
559: PetscCall(VecScale(ts->vecs_sensi2[nadj], 1. / th->shift));
560: }
561: }
563: th->stage_time = ts->ptime; /* recover the old value */
565: if (ts->vecs_sensip) { /* sensitivities wrt parameters */
566: /* U_{n+1} */
567: th->shift = 1.0 / (adjoint_time_step * th->Theta);
568: PetscCall(VecAXPBYPCZ(th->Xdot, -th->shift, th->shift, 0, th->X0, ts->vec_sol));
569: PetscCall(TSComputeIJacobianP(ts, th->stage_time, ts->vec_sol, th->Xdot, -1. / (th->Theta * adjoint_time_step), ts->Jacp, PETSC_FALSE));
570: if (quadts) PetscCall(TSComputeRHSJacobianP(quadts, th->stage_time, ts->vec_sol, quadJp));
571: for (nadj = 0; nadj < ts->numcost; nadj++) {
572: PetscCall(MatMultTranspose(ts->Jacp, VecsDeltaLam[nadj], VecsDeltaMu[nadj]));
573: PetscCall(VecAXPY(ts->vecs_sensip[nadj], -adjoint_time_step * th->Theta, VecsDeltaMu[nadj]));
574: if (quadJp) {
575: PetscCall(MatDenseGetColumn(quadJp, nadj, &xarr));
576: PetscCall(VecPlaceArray(ts->vec_drdp_col, xarr));
577: PetscCall(VecAXPY(ts->vecs_sensip[nadj], adjoint_time_step * th->Theta, ts->vec_drdp_col));
578: PetscCall(VecResetArray(ts->vec_drdp_col));
579: PetscCall(MatDenseRestoreColumn(quadJp, &xarr));
580: }
581: }
582: if (ts->vecs_sensi2p) { /* second-order */
583: /* Get w1 at t_{n+1} from TLM matrix */
584: PetscCall(MatDenseGetColumn(ts->mat_sensip, 0, &xarr));
585: PetscCall(VecPlaceArray(ts->vec_sensip_col, xarr));
586: /* lambda_s^T F_PU w_1 */
587: PetscCall(TSComputeIHessianProductFunctionPU(ts, th->stage_time, ts->vec_sol, VecsDeltaLam, ts->vec_sensip_col, ts->vecs_fpu));
588: PetscCall(VecResetArray(ts->vec_sensip_col));
589: PetscCall(MatDenseRestoreColumn(ts->mat_sensip, &xarr));
591: /* lambda_s^T F_PP w_2 */
592: PetscCall(TSComputeIHessianProductFunctionPP(ts, th->stage_time, ts->vec_sol, VecsDeltaLam, ts->vec_dir, ts->vecs_fpp));
593: for (nadj = 0; nadj < ts->numcost; nadj++) {
594: /* Mu2 <- Mu2 + h theta F_P^T Lambda_s + h theta (lambda_s^T F_UU w_1 + lambda_s^T F_UP w_2) */
595: PetscCall(MatMultTranspose(ts->Jacp, VecsDeltaLam2[nadj], VecsDeltaMu2[nadj]));
596: PetscCall(VecAXPY(ts->vecs_sensi2p[nadj], -adjoint_time_step * th->Theta, VecsDeltaMu2[nadj]));
597: if (ts->vecs_fpu) PetscCall(VecAXPY(ts->vecs_sensi2p[nadj], -adjoint_time_step * th->Theta, ts->vecs_fpu[nadj]));
598: if (ts->vecs_fpp) PetscCall(VecAXPY(ts->vecs_sensi2p[nadj], -adjoint_time_step * th->Theta, ts->vecs_fpp[nadj]));
599: }
600: }
602: /* U_s */
603: PetscCall(VecZeroEntries(th->Xdot));
604: PetscCall(TSComputeIJacobianP(ts, adjoint_ptime, th->X0, th->Xdot, 1. / ((th->Theta - 1.0) * adjoint_time_step), ts->Jacp, PETSC_FALSE));
605: if (quadts) PetscCall(TSComputeRHSJacobianP(quadts, adjoint_ptime, th->X0, quadJp));
606: for (nadj = 0; nadj < ts->numcost; nadj++) {
607: PetscCall(MatMultTranspose(ts->Jacp, VecsDeltaLam[nadj], VecsDeltaMu[nadj]));
608: PetscCall(VecAXPY(ts->vecs_sensip[nadj], -adjoint_time_step * (1.0 - th->Theta), VecsDeltaMu[nadj]));
609: if (quadJp) {
610: PetscCall(MatDenseGetColumn(quadJp, nadj, &xarr));
611: PetscCall(VecPlaceArray(ts->vec_drdp_col, xarr));
612: PetscCall(VecAXPY(ts->vecs_sensip[nadj], adjoint_time_step * (1.0 - th->Theta), ts->vec_drdp_col));
613: PetscCall(VecResetArray(ts->vec_drdp_col));
614: PetscCall(MatDenseRestoreColumn(quadJp, &xarr));
615: }
616: if (ts->vecs_sensi2p) { /* second-order */
617: /* Get w1 at t_n from TLM matrix */
618: PetscCall(MatDenseGetColumn(th->MatFwdSensip0, 0, &xarr));
619: PetscCall(VecPlaceArray(ts->vec_sensip_col, xarr));
620: /* lambda_s^T F_PU w_1 */
621: PetscCall(TSComputeIHessianProductFunctionPU(ts, adjoint_ptime, th->X0, VecsDeltaLam, ts->vec_sensip_col, ts->vecs_fpu));
622: PetscCall(VecResetArray(ts->vec_sensip_col));
623: PetscCall(MatDenseRestoreColumn(th->MatFwdSensip0, &xarr));
624: /* lambda_s^T F_PP w_2 */
625: PetscCall(TSComputeIHessianProductFunctionPP(ts, adjoint_ptime, th->X0, VecsDeltaLam, ts->vec_dir, ts->vecs_fpp));
626: for (nadj = 0; nadj < ts->numcost; nadj++) {
627: /* Mu2 <- Mu2 + h(1-theta) F_P^T Lambda_s + h(1-theta) (lambda_s^T F_UU w_1 + lambda_s^T F_UP w_2) */
628: PetscCall(MatMultTranspose(ts->Jacp, VecsDeltaLam2[nadj], VecsDeltaMu2[nadj]));
629: PetscCall(VecAXPY(ts->vecs_sensi2p[nadj], -adjoint_time_step * (1.0 - th->Theta), VecsDeltaMu2[nadj]));
630: if (ts->vecs_fpu) PetscCall(VecAXPY(ts->vecs_sensi2p[nadj], -adjoint_time_step * (1.0 - th->Theta), ts->vecs_fpu[nadj]));
631: if (ts->vecs_fpp) PetscCall(VecAXPY(ts->vecs_sensi2p[nadj], -adjoint_time_step * (1.0 - th->Theta), ts->vecs_fpp[nadj]));
632: }
633: }
634: }
635: }
636: } else { /* one-stage case */
637: th->shift = 0.0;
638: PetscCall(TSComputeSNESJacobian(ts, th->X, J, Jpre)); /* get -f_y */
639: PetscCall(KSPSetOperators(ksp, J, Jpre));
640: if (quadts) PetscCall(TSComputeRHSJacobian(quadts, th->stage_time, th->X, quadJ, NULL));
641: for (nadj = 0; nadj < ts->numcost; nadj++) {
642: PetscCall(MatMultTranspose(J, VecsDeltaLam[nadj], VecsSensiTemp[nadj]));
643: PetscCall(VecAXPY(ts->vecs_sensi[nadj], -adjoint_time_step, VecsSensiTemp[nadj]));
644: if (quadJ) {
645: PetscCall(MatDenseGetColumn(quadJ, nadj, &xarr));
646: PetscCall(VecPlaceArray(ts->vec_drdu_col, xarr));
647: PetscCall(VecAXPY(ts->vecs_sensi[nadj], adjoint_time_step, ts->vec_drdu_col));
648: PetscCall(VecResetArray(ts->vec_drdu_col));
649: PetscCall(MatDenseRestoreColumn(quadJ, &xarr));
650: }
651: }
652: if (ts->vecs_sensip) {
653: th->shift = 1.0 / (adjoint_time_step * th->Theta);
654: PetscCall(VecAXPBYPCZ(th->Xdot, -th->shift, th->shift, 0, th->X0, th->X));
655: PetscCall(TSComputeIJacobianP(ts, th->stage_time, th->X, th->Xdot, th->shift, ts->Jacp, PETSC_FALSE));
656: if (quadts) PetscCall(TSComputeRHSJacobianP(quadts, th->stage_time, th->X, quadJp));
657: for (nadj = 0; nadj < ts->numcost; nadj++) {
658: PetscCall(MatMultTranspose(ts->Jacp, VecsDeltaLam[nadj], VecsDeltaMu[nadj]));
659: PetscCall(VecAXPY(ts->vecs_sensip[nadj], -adjoint_time_step, VecsDeltaMu[nadj]));
660: if (quadJp) {
661: PetscCall(MatDenseGetColumn(quadJp, nadj, &xarr));
662: PetscCall(VecPlaceArray(ts->vec_drdp_col, xarr));
663: PetscCall(VecAXPY(ts->vecs_sensip[nadj], adjoint_time_step, ts->vec_drdp_col));
664: PetscCall(VecResetArray(ts->vec_drdp_col));
665: PetscCall(MatDenseRestoreColumn(quadJp, &xarr));
666: }
667: }
668: }
669: }
671: th->status = TS_STEP_COMPLETE;
672: PetscFunctionReturn(PETSC_SUCCESS);
673: }
675: static PetscErrorCode TSInterpolate_Theta(TS ts, PetscReal t, Vec X)
676: {
677: TS_Theta *th = (TS_Theta *)ts->data;
678: PetscReal dt = t - ts->ptime;
680: PetscFunctionBegin;
681: PetscCall(VecCopy(ts->vec_sol, th->X));
682: if (th->endpoint) dt *= th->Theta;
683: PetscCall(VecWAXPY(X, dt, th->Xdot, th->X));
684: PetscFunctionReturn(PETSC_SUCCESS);
685: }
687: static PetscErrorCode TSEvaluateWLTE_Theta(TS ts, NormType wnormtype, PetscInt *order, PetscReal *wlte)
688: {
689: TS_Theta *th = (TS_Theta *)ts->data;
690: Vec X = ts->vec_sol; /* X = solution */
691: Vec Y = th->vec_lte_work; /* Y = X + LTE */
692: PetscReal wltea, wlter;
694: PetscFunctionBegin;
695: if (!th->vec_sol_prev) {
696: *wlte = -1;
697: PetscFunctionReturn(PETSC_SUCCESS);
698: }
699: /* Cannot compute LTE in first step or in restart after event */
700: if (ts->steprestart) {
701: *wlte = -1;
702: PetscFunctionReturn(PETSC_SUCCESS);
703: }
704: /* Compute LTE using backward differences with non-constant time step */
705: {
706: PetscReal h = ts->time_step, h_prev = ts->ptime - ts->ptime_prev;
707: PetscReal a = 1 + h_prev / h;
708: PetscScalar scal[3];
709: Vec vecs[3];
711: scal[0] = -1 / a;
712: scal[1] = +1 / (a - 1);
713: scal[2] = -1 / (a * (a - 1));
714: vecs[0] = X;
715: vecs[1] = th->X0;
716: vecs[2] = th->vec_sol_prev;
717: PetscCall(VecCopy(X, Y));
718: PetscCall(VecMAXPY(Y, 3, scal, vecs));
719: PetscCall(TSErrorWeightedNorm(ts, X, Y, wnormtype, wlte, &wltea, &wlter));
720: }
721: if (order) *order = 2;
722: PetscFunctionReturn(PETSC_SUCCESS);
723: }
725: static PetscErrorCode TSRollBack_Theta(TS ts)
726: {
727: TS_Theta *th = (TS_Theta *)ts->data;
728: TS quadts = ts->quadraturets;
730: PetscFunctionBegin;
731: if (quadts && ts->costintegralfwd) PetscCall(VecCopy(th->VecCostIntegral0, quadts->vec_sol));
732: th->status = TS_STEP_INCOMPLETE;
733: if (ts->mat_sensip) PetscCall(MatCopy(th->MatFwdSensip0, ts->mat_sensip, SAME_NONZERO_PATTERN));
734: if (quadts && quadts->mat_sensip) PetscCall(MatCopy(th->MatIntegralSensip0, quadts->mat_sensip, SAME_NONZERO_PATTERN));
735: PetscFunctionReturn(PETSC_SUCCESS);
736: }
738: static PetscErrorCode TSForwardStep_Theta(TS ts)
739: {
740: TS_Theta *th = (TS_Theta *)ts->data;
741: TS quadts = ts->quadraturets;
742: Mat MatDeltaFwdSensip = th->MatDeltaFwdSensip;
743: Vec VecDeltaFwdSensipCol = th->VecDeltaFwdSensipCol;
744: PetscInt ntlm;
745: KSP ksp;
746: Mat J, Jpre, quadJ = NULL, quadJp = NULL;
747: PetscScalar *barr, *xarr;
748: PetscReal previous_shift;
750: PetscFunctionBegin;
751: previous_shift = th->shift;
752: PetscCall(MatCopy(ts->mat_sensip, th->MatFwdSensip0, SAME_NONZERO_PATTERN));
754: if (quadts && quadts->mat_sensip) PetscCall(MatCopy(quadts->mat_sensip, th->MatIntegralSensip0, SAME_NONZERO_PATTERN));
755: PetscCall(SNESGetKSP(ts->snes, &ksp));
756: PetscCall(TSGetIJacobian(ts, &J, &Jpre, NULL, NULL));
757: if (quadts) {
758: PetscCall(TSGetRHSJacobian(quadts, &quadJ, NULL, NULL, NULL));
759: PetscCall(TSGetRHSJacobianP(quadts, &quadJp, NULL, NULL));
760: }
762: /* Build RHS */
763: if (th->endpoint) { /* 2-stage method*/
764: th->shift = 1. / ((th->Theta - 1.) * th->time_step0);
765: PetscCall(TSComputeIJacobian(ts, th->ptime0, th->X0, th->Xdot, th->shift, J, Jpre, PETSC_FALSE));
766: PetscCall(MatMatMult(J, ts->mat_sensip, MAT_REUSE_MATRIX, PETSC_DETERMINE, &MatDeltaFwdSensip));
767: PetscCall(MatScale(MatDeltaFwdSensip, (th->Theta - 1.) / th->Theta));
769: /* Add the f_p forcing terms */
770: if (ts->Jacp) {
771: PetscCall(VecZeroEntries(th->Xdot));
772: PetscCall(TSComputeIJacobianP(ts, th->ptime0, th->X0, th->Xdot, th->shift, ts->Jacp, PETSC_FALSE));
773: PetscCall(MatAXPY(MatDeltaFwdSensip, (th->Theta - 1.) / th->Theta, ts->Jacp, SUBSET_NONZERO_PATTERN));
774: th->shift = previous_shift;
775: PetscCall(VecAXPBYPCZ(th->Xdot, -th->shift, th->shift, 0, th->X0, ts->vec_sol));
776: PetscCall(TSComputeIJacobianP(ts, th->stage_time, ts->vec_sol, th->Xdot, th->shift, ts->Jacp, PETSC_FALSE));
777: PetscCall(MatAXPY(MatDeltaFwdSensip, -1., ts->Jacp, SUBSET_NONZERO_PATTERN));
778: }
779: } else { /* 1-stage method */
780: th->shift = 0.0;
781: PetscCall(TSComputeIJacobian(ts, th->stage_time, th->X, th->Xdot, th->shift, J, Jpre, PETSC_FALSE));
782: PetscCall(MatMatMult(J, ts->mat_sensip, MAT_REUSE_MATRIX, PETSC_DETERMINE, &MatDeltaFwdSensip));
783: PetscCall(MatScale(MatDeltaFwdSensip, -1.));
785: /* Add the f_p forcing terms */
786: if (ts->Jacp) {
787: th->shift = previous_shift;
788: PetscCall(VecAXPBYPCZ(th->Xdot, -th->shift, th->shift, 0, th->X0, th->X));
789: PetscCall(TSComputeIJacobianP(ts, th->stage_time, th->X, th->Xdot, th->shift, ts->Jacp, PETSC_FALSE));
790: PetscCall(MatAXPY(MatDeltaFwdSensip, -1., ts->Jacp, SUBSET_NONZERO_PATTERN));
791: }
792: }
794: /* Build LHS */
795: th->shift = previous_shift; /* recover the previous shift used in TSStep_Theta() */
796: if (th->endpoint) {
797: PetscCall(TSComputeIJacobian(ts, th->stage_time, ts->vec_sol, th->Xdot, th->shift, J, Jpre, PETSC_FALSE));
798: } else {
799: PetscCall(TSComputeIJacobian(ts, th->stage_time, th->X, th->Xdot, th->shift, J, Jpre, PETSC_FALSE));
800: }
801: PetscCall(KSPSetOperators(ksp, J, Jpre));
803: /*
804: Evaluate the first stage of integral gradients with the 2-stage method:
805: drdu|t_n*S(t_n) + drdp|t_n
806: This is done before the linear solve because the sensitivity variable S(t_n) will be propagated to S(t_{n+1})
807: */
808: if (th->endpoint) { /* 2-stage method only */
809: if (quadts && quadts->mat_sensip) {
810: PetscCall(TSComputeRHSJacobian(quadts, th->ptime0, th->X0, quadJ, NULL));
811: PetscCall(TSComputeRHSJacobianP(quadts, th->ptime0, th->X0, quadJp));
812: PetscCall(MatTransposeMatMult(ts->mat_sensip, quadJ, MAT_REUSE_MATRIX, PETSC_DETERMINE, &th->MatIntegralSensipTemp));
813: PetscCall(MatAXPY(th->MatIntegralSensipTemp, 1, quadJp, SAME_NONZERO_PATTERN));
814: PetscCall(MatAXPY(quadts->mat_sensip, th->time_step0 * (1. - th->Theta), th->MatIntegralSensipTemp, SAME_NONZERO_PATTERN));
815: }
816: }
818: /* Solve the tangent linear equation for forward sensitivities to parameters */
819: for (ntlm = 0; ntlm < th->num_tlm; ntlm++) {
820: KSPConvergedReason kspreason;
821: PetscCall(MatDenseGetColumn(MatDeltaFwdSensip, ntlm, &barr));
822: PetscCall(VecPlaceArray(VecDeltaFwdSensipCol, barr));
823: if (th->endpoint) {
824: PetscCall(MatDenseGetColumn(ts->mat_sensip, ntlm, &xarr));
825: PetscCall(VecPlaceArray(ts->vec_sensip_col, xarr));
826: PetscCall(KSPSolve(ksp, VecDeltaFwdSensipCol, ts->vec_sensip_col));
827: PetscCall(VecResetArray(ts->vec_sensip_col));
828: PetscCall(MatDenseRestoreColumn(ts->mat_sensip, &xarr));
829: } else {
830: PetscCall(KSPSolve(ksp, VecDeltaFwdSensipCol, VecDeltaFwdSensipCol));
831: }
832: PetscCall(KSPGetConvergedReason(ksp, &kspreason));
833: if (kspreason < 0) {
834: ts->reason = TSFORWARD_DIVERGED_LINEAR_SOLVE;
835: PetscCall(PetscInfo(ts, "Step=%" PetscInt_FMT ", %" PetscInt_FMT "th tangent linear solve, linear solve fails, stopping tangent linear solve\n", ts->steps, ntlm));
836: }
837: PetscCall(VecResetArray(VecDeltaFwdSensipCol));
838: PetscCall(MatDenseRestoreColumn(MatDeltaFwdSensip, &barr));
839: }
841: /*
842: Evaluate the second stage of integral gradients with the 2-stage method:
843: drdu|t_{n+1}*S(t_{n+1}) + drdp|t_{n+1}
844: */
845: if (quadts && quadts->mat_sensip) {
846: if (!th->endpoint) {
847: PetscCall(MatAXPY(ts->mat_sensip, 1, MatDeltaFwdSensip, SAME_NONZERO_PATTERN)); /* stage sensitivity */
848: PetscCall(TSComputeRHSJacobian(quadts, th->stage_time, th->X, quadJ, NULL));
849: PetscCall(TSComputeRHSJacobianP(quadts, th->stage_time, th->X, quadJp));
850: PetscCall(MatTransposeMatMult(ts->mat_sensip, quadJ, MAT_REUSE_MATRIX, PETSC_DETERMINE, &th->MatIntegralSensipTemp));
851: PetscCall(MatAXPY(th->MatIntegralSensipTemp, 1, quadJp, SAME_NONZERO_PATTERN));
852: PetscCall(MatAXPY(quadts->mat_sensip, th->time_step0, th->MatIntegralSensipTemp, SAME_NONZERO_PATTERN));
853: PetscCall(MatAXPY(ts->mat_sensip, (1. - th->Theta) / th->Theta, MatDeltaFwdSensip, SAME_NONZERO_PATTERN));
854: } else {
855: PetscCall(TSComputeRHSJacobian(quadts, th->stage_time, ts->vec_sol, quadJ, NULL));
856: PetscCall(TSComputeRHSJacobianP(quadts, th->stage_time, ts->vec_sol, quadJp));
857: PetscCall(MatTransposeMatMult(ts->mat_sensip, quadJ, MAT_REUSE_MATRIX, PETSC_DETERMINE, &th->MatIntegralSensipTemp));
858: PetscCall(MatAXPY(th->MatIntegralSensipTemp, 1, quadJp, SAME_NONZERO_PATTERN));
859: PetscCall(MatAXPY(quadts->mat_sensip, th->time_step0 * th->Theta, th->MatIntegralSensipTemp, SAME_NONZERO_PATTERN));
860: }
861: } else {
862: if (!th->endpoint) PetscCall(MatAXPY(ts->mat_sensip, 1. / th->Theta, MatDeltaFwdSensip, SAME_NONZERO_PATTERN));
863: }
864: PetscFunctionReturn(PETSC_SUCCESS);
865: }
867: static PetscErrorCode TSForwardGetStages_Theta(TS ts, PetscInt *ns, Mat *stagesensip[])
868: {
869: TS_Theta *th = (TS_Theta *)ts->data;
871: PetscFunctionBegin;
872: if (ns) {
873: if (!th->endpoint && th->Theta != 1.0) *ns = 1; /* midpoint form */
874: else *ns = 2; /* endpoint form */
875: }
876: if (stagesensip) {
877: if (!th->endpoint && th->Theta != 1.0) {
878: th->MatFwdStages[0] = th->MatDeltaFwdSensip;
879: } else {
880: th->MatFwdStages[0] = th->MatFwdSensip0;
881: th->MatFwdStages[1] = ts->mat_sensip; /* stiffly accurate */
882: }
883: *stagesensip = th->MatFwdStages;
884: }
885: PetscFunctionReturn(PETSC_SUCCESS);
886: }
888: static PetscErrorCode TSReset_Theta(TS ts)
889: {
890: TS_Theta *th = (TS_Theta *)ts->data;
892: PetscFunctionBegin;
893: PetscCall(VecDestroy(&th->X));
894: PetscCall(VecDestroy(&th->Xdot));
895: PetscCall(VecDestroy(&th->X0));
896: PetscCall(VecDestroy(&th->affine));
898: PetscCall(VecDestroy(&th->vec_sol_prev));
899: PetscCall(VecDestroy(&th->vec_lte_work));
901: PetscCall(VecDestroy(&th->VecCostIntegral0));
902: PetscFunctionReturn(PETSC_SUCCESS);
903: }
905: static PetscErrorCode TSAdjointReset_Theta(TS ts)
906: {
907: TS_Theta *th = (TS_Theta *)ts->data;
909: PetscFunctionBegin;
910: PetscCall(VecDestroyVecs(ts->numcost, &th->VecsDeltaLam));
911: PetscCall(VecDestroyVecs(ts->numcost, &th->VecsDeltaMu));
912: PetscCall(VecDestroyVecs(ts->numcost, &th->VecsDeltaLam2));
913: PetscCall(VecDestroyVecs(ts->numcost, &th->VecsDeltaMu2));
914: PetscCall(VecDestroyVecs(ts->numcost, &th->VecsSensiTemp));
915: PetscCall(VecDestroyVecs(ts->numcost, &th->VecsSensi2Temp));
916: PetscFunctionReturn(PETSC_SUCCESS);
917: }
919: static PetscErrorCode TSDestroy_Theta(TS ts)
920: {
921: PetscFunctionBegin;
922: PetscCall(TSReset_Theta(ts));
923: if (ts->dm) {
924: PetscCall(DMCoarsenHookRemove(ts->dm, DMCoarsenHook_TSTheta, DMRestrictHook_TSTheta, ts));
925: PetscCall(DMSubDomainHookRemove(ts->dm, DMSubDomainHook_TSTheta, DMSubDomainRestrictHook_TSTheta, ts));
926: }
927: PetscCall(PetscFree(ts->data));
928: PetscCall(PetscObjectComposeFunction((PetscObject)ts, "TSThetaGetTheta_C", NULL));
929: PetscCall(PetscObjectComposeFunction((PetscObject)ts, "TSThetaSetTheta_C", NULL));
930: PetscCall(PetscObjectComposeFunction((PetscObject)ts, "TSThetaGetEndpoint_C", NULL));
931: PetscCall(PetscObjectComposeFunction((PetscObject)ts, "TSThetaSetEndpoint_C", NULL));
932: PetscFunctionReturn(PETSC_SUCCESS);
933: }
935: /*
936: This defines the nonlinear equation that is to be solved with SNES
937: G(U) = F[t0+Theta*dt, U, (U-U0)*shift] = 0
939: Note that U here is the stage argument. This means that U = U_{n+1} only if endpoint = true,
940: otherwise U = theta U_{n+1} + (1 - theta) U0, which for the case of implicit midpoint is
941: U = (U_{n+1} + U0)/2
942: */
943: static PetscErrorCode SNESTSFormFunction_Theta(SNES snes, Vec x, Vec y, TS ts)
944: {
945: TS_Theta *th = (TS_Theta *)ts->data;
946: Vec X0, Xdot;
947: DM dm, dmsave;
948: PetscReal shift = th->shift;
950: PetscFunctionBegin;
951: PetscCall(SNESGetDM(snes, &dm));
952: /* When using the endpoint variant, this is actually 1/Theta * Xdot */
953: PetscCall(TSThetaGetX0AndXdot(ts, dm, &X0, &Xdot));
954: if (x != X0) {
955: PetscCall(VecAXPBYPCZ(Xdot, -shift, shift, 0, X0, x));
956: } else {
957: PetscCall(VecZeroEntries(Xdot));
958: }
959: /* DM monkey-business allows user code to call TSGetDM() inside of functions evaluated on levels of FAS */
960: dmsave = ts->dm;
961: ts->dm = dm;
962: PetscCall(TSComputeIFunction(ts, th->stage_time, x, Xdot, y, PETSC_FALSE));
963: ts->dm = dmsave;
964: PetscCall(TSThetaRestoreX0AndXdot(ts, dm, &X0, &Xdot));
965: PetscFunctionReturn(PETSC_SUCCESS);
966: }
968: static PetscErrorCode SNESTSFormJacobian_Theta(SNES snes, Vec x, Mat A, Mat B, TS ts)
969: {
970: TS_Theta *th = (TS_Theta *)ts->data;
971: Vec Xdot;
972: DM dm, dmsave;
973: PetscReal shift = th->shift;
975: PetscFunctionBegin;
976: PetscCall(SNESGetDM(snes, &dm));
977: /* Xdot has already been computed in SNESTSFormFunction_Theta (SNES guarantees this) */
978: PetscCall(TSThetaGetX0AndXdot(ts, dm, NULL, &Xdot));
980: dmsave = ts->dm;
981: ts->dm = dm;
982: PetscCall(TSComputeIJacobian(ts, th->stage_time, x, Xdot, shift, A, B, PETSC_FALSE));
983: ts->dm = dmsave;
984: PetscCall(TSThetaRestoreX0AndXdot(ts, dm, NULL, &Xdot));
985: PetscFunctionReturn(PETSC_SUCCESS);
986: }
988: static PetscErrorCode TSForwardSetUp_Theta(TS ts)
989: {
990: TS_Theta *th = (TS_Theta *)ts->data;
991: TS quadts = ts->quadraturets;
993: PetscFunctionBegin;
994: /* combine sensitivities to parameters and sensitivities to initial values into one array */
995: th->num_tlm = ts->num_parameters;
996: PetscCall(MatDuplicate(ts->mat_sensip, MAT_DO_NOT_COPY_VALUES, &th->MatDeltaFwdSensip));
997: if (quadts && quadts->mat_sensip) {
998: PetscCall(MatDuplicate(quadts->mat_sensip, MAT_DO_NOT_COPY_VALUES, &th->MatIntegralSensipTemp));
999: PetscCall(MatDuplicate(quadts->mat_sensip, MAT_DO_NOT_COPY_VALUES, &th->MatIntegralSensip0));
1000: }
1001: /* backup sensitivity results for roll-backs */
1002: PetscCall(MatDuplicate(ts->mat_sensip, MAT_DO_NOT_COPY_VALUES, &th->MatFwdSensip0));
1004: PetscCall(VecDuplicate(ts->vec_sol, &th->VecDeltaFwdSensipCol));
1005: PetscFunctionReturn(PETSC_SUCCESS);
1006: }
1008: static PetscErrorCode TSForwardReset_Theta(TS ts)
1009: {
1010: TS_Theta *th = (TS_Theta *)ts->data;
1011: TS quadts = ts->quadraturets;
1013: PetscFunctionBegin;
1014: if (quadts && quadts->mat_sensip) {
1015: PetscCall(MatDestroy(&th->MatIntegralSensipTemp));
1016: PetscCall(MatDestroy(&th->MatIntegralSensip0));
1017: }
1018: PetscCall(VecDestroy(&th->VecDeltaFwdSensipCol));
1019: PetscCall(MatDestroy(&th->MatDeltaFwdSensip));
1020: PetscCall(MatDestroy(&th->MatFwdSensip0));
1021: PetscFunctionReturn(PETSC_SUCCESS);
1022: }
1024: static PetscErrorCode TSSetUp_Theta(TS ts)
1025: {
1026: TS_Theta *th = (TS_Theta *)ts->data;
1027: TS quadts = ts->quadraturets;
1028: PetscBool match;
1030: PetscFunctionBegin;
1031: if (!th->VecCostIntegral0 && quadts && ts->costintegralfwd) { /* back up cost integral */
1032: PetscCall(VecDuplicate(quadts->vec_sol, &th->VecCostIntegral0));
1033: }
1034: if (!th->X) PetscCall(VecDuplicate(ts->vec_sol, &th->X));
1035: if (!th->Xdot) PetscCall(VecDuplicate(ts->vec_sol, &th->Xdot));
1036: if (!th->X0) PetscCall(VecDuplicate(ts->vec_sol, &th->X0));
1037: if (th->endpoint) PetscCall(VecDuplicate(ts->vec_sol, &th->affine));
1039: th->order = (th->Theta == 0.5) ? 2 : 1;
1040: th->shift = 1 / (th->Theta * ts->time_step);
1042: PetscCall(TSGetDM(ts, &ts->dm));
1043: PetscCall(DMCoarsenHookAdd(ts->dm, DMCoarsenHook_TSTheta, DMRestrictHook_TSTheta, ts));
1044: PetscCall(DMSubDomainHookAdd(ts->dm, DMSubDomainHook_TSTheta, DMSubDomainRestrictHook_TSTheta, ts));
1046: PetscCall(TSGetAdapt(ts, &ts->adapt));
1047: PetscCall(TSAdaptCandidatesClear(ts->adapt));
1048: PetscCall(PetscObjectTypeCompare((PetscObject)ts->adapt, TSADAPTNONE, &match));
1049: if (!match) {
1050: if (!th->vec_sol_prev) PetscCall(VecDuplicate(ts->vec_sol, &th->vec_sol_prev));
1051: if (!th->vec_lte_work) PetscCall(VecDuplicate(ts->vec_sol, &th->vec_lte_work));
1052: }
1053: PetscCall(TSGetSNES(ts, &ts->snes));
1055: ts->stifflyaccurate = (!th->endpoint && th->Theta != 1.0) ? PETSC_FALSE : PETSC_TRUE;
1056: PetscFunctionReturn(PETSC_SUCCESS);
1057: }
1059: static PetscErrorCode TSAdjointSetUp_Theta(TS ts)
1060: {
1061: TS_Theta *th = (TS_Theta *)ts->data;
1063: PetscFunctionBegin;
1064: PetscCall(VecDuplicateVecs(ts->vecs_sensi[0], ts->numcost, &th->VecsDeltaLam));
1065: PetscCall(VecDuplicateVecs(ts->vecs_sensi[0], ts->numcost, &th->VecsSensiTemp));
1066: if (ts->vecs_sensip) PetscCall(VecDuplicateVecs(ts->vecs_sensip[0], ts->numcost, &th->VecsDeltaMu));
1067: if (ts->vecs_sensi2) {
1068: PetscCall(VecDuplicateVecs(ts->vecs_sensi[0], ts->numcost, &th->VecsDeltaLam2));
1069: PetscCall(VecDuplicateVecs(ts->vecs_sensi2[0], ts->numcost, &th->VecsSensi2Temp));
1070: /* hack ts to make implicit TS solver work when users provide only explicit versions of callbacks (RHSFunction,RHSJacobian,RHSHessian etc.) */
1071: if (!ts->ihessianproduct_fuu) ts->vecs_fuu = ts->vecs_guu;
1072: if (!ts->ihessianproduct_fup) ts->vecs_fup = ts->vecs_gup;
1073: }
1074: if (ts->vecs_sensi2p) {
1075: PetscCall(VecDuplicateVecs(ts->vecs_sensi2p[0], ts->numcost, &th->VecsDeltaMu2));
1076: /* hack ts to make implicit TS solver work when users provide only explicit versions of callbacks (RHSFunction,RHSJacobian,RHSHessian etc.) */
1077: if (!ts->ihessianproduct_fpu) ts->vecs_fpu = ts->vecs_gpu;
1078: if (!ts->ihessianproduct_fpp) ts->vecs_fpp = ts->vecs_gpp;
1079: }
1080: PetscFunctionReturn(PETSC_SUCCESS);
1081: }
1083: static PetscErrorCode TSSetFromOptions_Theta(TS ts, PetscOptionItems PetscOptionsObject)
1084: {
1085: TS_Theta *th = (TS_Theta *)ts->data;
1087: PetscFunctionBegin;
1088: PetscOptionsHeadBegin(PetscOptionsObject, "Theta ODE solver options");
1089: {
1090: PetscCall(PetscOptionsReal("-ts_theta_theta", "Location of stage (0<Theta<=1)", "TSThetaSetTheta", th->Theta, &th->Theta, NULL));
1091: PetscCall(PetscOptionsBool("-ts_theta_endpoint", "Use the endpoint instead of midpoint form of the Theta method", "TSThetaSetEndpoint", th->endpoint, &th->endpoint, NULL));
1092: PetscCall(PetscOptionsBool("-ts_theta_initial_guess_extrapolate", "Extrapolate stage initial guess from previous solution (sometimes unstable)", "TSThetaSetExtrapolate", th->extrapolate, &th->extrapolate, NULL));
1093: }
1094: PetscOptionsHeadEnd();
1095: PetscFunctionReturn(PETSC_SUCCESS);
1096: }
1098: static PetscErrorCode TSView_Theta(TS ts, PetscViewer viewer)
1099: {
1100: TS_Theta *th = (TS_Theta *)ts->data;
1101: PetscBool isascii;
1103: PetscFunctionBegin;
1104: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1105: if (isascii) {
1106: PetscCall(PetscViewerASCIIPrintf(viewer, " Theta=%g\n", (double)th->Theta));
1107: PetscCall(PetscViewerASCIIPrintf(viewer, " Extrapolation=%s\n", th->extrapolate ? "yes" : "no"));
1108: }
1109: PetscFunctionReturn(PETSC_SUCCESS);
1110: }
1112: static PetscErrorCode TSThetaGetTheta_Theta(TS ts, PetscReal *theta)
1113: {
1114: TS_Theta *th = (TS_Theta *)ts->data;
1116: PetscFunctionBegin;
1117: *theta = th->Theta;
1118: PetscFunctionReturn(PETSC_SUCCESS);
1119: }
1121: static PetscErrorCode TSThetaSetTheta_Theta(TS ts, PetscReal theta)
1122: {
1123: TS_Theta *th = (TS_Theta *)ts->data;
1125: PetscFunctionBegin;
1126: PetscCheck(theta > 0 && theta <= 1, PetscObjectComm((PetscObject)ts), PETSC_ERR_ARG_OUTOFRANGE, "Theta %g not in range (0,1]", (double)theta);
1127: th->Theta = theta;
1128: th->order = (th->Theta == 0.5) ? 2 : 1;
1129: PetscFunctionReturn(PETSC_SUCCESS);
1130: }
1132: static PetscErrorCode TSThetaGetEndpoint_Theta(TS ts, PetscBool *endpoint)
1133: {
1134: TS_Theta *th = (TS_Theta *)ts->data;
1136: PetscFunctionBegin;
1137: *endpoint = th->endpoint;
1138: PetscFunctionReturn(PETSC_SUCCESS);
1139: }
1141: static PetscErrorCode TSThetaSetEndpoint_Theta(TS ts, PetscBool flg)
1142: {
1143: TS_Theta *th = (TS_Theta *)ts->data;
1145: PetscFunctionBegin;
1146: th->endpoint = flg;
1147: PetscFunctionReturn(PETSC_SUCCESS);
1148: }
1150: #if defined(PETSC_HAVE_COMPLEX)
1151: static PetscErrorCode TSComputeLinearStability_Theta(TS ts, PetscReal xr, PetscReal xi, PetscReal *yr, PetscReal *yi)
1152: {
1153: PetscComplex z = xr + xi * PETSC_i, f;
1154: TS_Theta *th = (TS_Theta *)ts->data;
1156: PetscFunctionBegin;
1157: f = (1.0 + (1.0 - th->Theta) * z) / (1.0 - th->Theta * z);
1158: *yr = PetscRealPartComplex(f);
1159: *yi = PetscImaginaryPartComplex(f);
1160: PetscFunctionReturn(PETSC_SUCCESS);
1161: }
1162: #endif
1164: static PetscErrorCode TSGetStages_Theta(TS ts, PetscInt *ns, Vec *Y[])
1165: {
1166: TS_Theta *th = (TS_Theta *)ts->data;
1168: PetscFunctionBegin;
1169: if (ns) {
1170: if (!th->endpoint && th->Theta != 1.0) *ns = 1; /* midpoint form */
1171: else *ns = 2; /* endpoint form */
1172: }
1173: if (Y) {
1174: if (!th->endpoint && th->Theta != 1.0) {
1175: th->Stages[0] = th->X;
1176: } else {
1177: th->Stages[0] = th->X0;
1178: th->Stages[1] = ts->vec_sol; /* stiffly accurate */
1179: }
1180: *Y = th->Stages;
1181: }
1182: PetscFunctionReturn(PETSC_SUCCESS);
1183: }
1185: /*MC
1186: TSTHETA - DAE solver using the implicit Theta method
1188: Level: beginner
1190: Options Database Keys:
1191: + -ts_theta_theta <Theta> - Location of stage (0<Theta<=1)
1192: . -ts_theta_endpoint <flag> - Use the endpoint (like Crank-Nicholson) instead of midpoint form of the Theta method
1193: - -ts_theta_initial_guess_extrapolate <flg> - Extrapolate stage initial guess from previous solution (sometimes unstable)
1195: Notes:
1196: .vb
1197: -ts_type theta -ts_theta_theta 1.0 corresponds to backward Euler (TSBEULER)
1198: -ts_type theta -ts_theta_theta 0.5 corresponds to the implicit midpoint rule
1199: -ts_type theta -ts_theta_theta 0.5 -ts_theta_endpoint corresponds to Crank-Nicholson (TSCN)
1200: .ve
1202: The endpoint variant of the Theta method and backward Euler can be applied to DAE. The midpoint variant is not suitable for DAEs because it is not stiffly accurate.
1204: The midpoint variant is cast as a 1-stage implicit Runge-Kutta method.
1206: .vb
1207: Theta | Theta
1208: -------------
1209: | 1
1210: .ve
1212: For the default Theta=0.5, this is also known as the implicit midpoint rule.
1214: When the endpoint variant is chosen, the method becomes a 2-stage method with first stage explicit:
1216: .vb
1217: 0 | 0 0
1218: 1 | 1-Theta Theta
1219: -------------------
1220: | 1-Theta Theta
1221: .ve
1223: For the default Theta=0.5, this is the trapezoid rule (also known as Crank-Nicolson, see TSCN).
1225: To apply a diagonally implicit RK method to DAE, the stage formula
1226: .vb
1227: Y_i = X + h sum_j a_ij Y'_j
1228: .ve
1229: is interpreted as a formula for Y'_i in terms of Y_i and known values (Y'_j, j<i)
1231: .seealso: [](ch_ts), `TSCreate()`, `TS`, `TSSetType()`, `TSCN`, `TSBEULER`, `TSThetaSetTheta()`, `TSThetaSetEndpoint()`
1232: M*/
1233: PETSC_EXTERN PetscErrorCode TSCreate_Theta(TS ts)
1234: {
1235: TS_Theta *th;
1237: PetscFunctionBegin;
1238: ts->ops->reset = TSReset_Theta;
1239: ts->ops->adjointreset = TSAdjointReset_Theta;
1240: ts->ops->destroy = TSDestroy_Theta;
1241: ts->ops->view = TSView_Theta;
1242: ts->ops->setup = TSSetUp_Theta;
1243: ts->ops->adjointsetup = TSAdjointSetUp_Theta;
1244: ts->ops->adjointreset = TSAdjointReset_Theta;
1245: ts->ops->step = TSStep_Theta;
1246: ts->ops->interpolate = TSInterpolate_Theta;
1247: ts->ops->evaluatewlte = TSEvaluateWLTE_Theta;
1248: ts->ops->rollback = TSRollBack_Theta;
1249: ts->ops->resizeregister = TSResizeRegister_Theta;
1250: ts->ops->setfromoptions = TSSetFromOptions_Theta;
1251: ts->ops->snesfunction = SNESTSFormFunction_Theta;
1252: ts->ops->snesjacobian = SNESTSFormJacobian_Theta;
1253: #if defined(PETSC_HAVE_COMPLEX)
1254: ts->ops->linearstability = TSComputeLinearStability_Theta;
1255: #endif
1256: ts->ops->getstages = TSGetStages_Theta;
1257: ts->ops->adjointstep = TSAdjointStep_Theta;
1258: ts->ops->adjointintegral = TSAdjointCostIntegral_Theta;
1259: ts->ops->forwardintegral = TSForwardCostIntegral_Theta;
1260: ts->default_adapt_type = TSADAPTNONE;
1262: ts->ops->forwardsetup = TSForwardSetUp_Theta;
1263: ts->ops->forwardreset = TSForwardReset_Theta;
1264: ts->ops->forwardstep = TSForwardStep_Theta;
1265: ts->ops->forwardgetstages = TSForwardGetStages_Theta;
1267: ts->usessnes = PETSC_TRUE;
1269: PetscCall(PetscNew(&th));
1270: ts->data = (void *)th;
1272: th->VecsDeltaLam = NULL;
1273: th->VecsDeltaMu = NULL;
1274: th->VecsSensiTemp = NULL;
1275: th->VecsSensi2Temp = NULL;
1277: th->extrapolate = PETSC_FALSE;
1278: th->Theta = 0.5;
1279: th->order = 2;
1280: PetscCall(PetscObjectComposeFunction((PetscObject)ts, "TSThetaGetTheta_C", TSThetaGetTheta_Theta));
1281: PetscCall(PetscObjectComposeFunction((PetscObject)ts, "TSThetaSetTheta_C", TSThetaSetTheta_Theta));
1282: PetscCall(PetscObjectComposeFunction((PetscObject)ts, "TSThetaGetEndpoint_C", TSThetaGetEndpoint_Theta));
1283: PetscCall(PetscObjectComposeFunction((PetscObject)ts, "TSThetaSetEndpoint_C", TSThetaSetEndpoint_Theta));
1284: PetscFunctionReturn(PETSC_SUCCESS);
1285: }
1287: /*@
1288: TSThetaGetTheta - Get the abscissa of the stage in (0,1] for `TSTHETA`
1290: Not Collective
1292: Input Parameter:
1293: . ts - timestepping context
1295: Output Parameter:
1296: . theta - stage abscissa
1298: Level: advanced
1300: Note:
1301: Use of this function is normally only required to hack `TSTHETA` to use a modified integration scheme.
1303: .seealso: [](ch_ts), `TSThetaSetTheta()`, `TSTHETA`
1304: @*/
1305: PetscErrorCode TSThetaGetTheta(TS ts, PetscReal *theta)
1306: {
1307: PetscFunctionBegin;
1309: PetscAssertPointer(theta, 2);
1310: PetscUseMethod(ts, "TSThetaGetTheta_C", (TS, PetscReal *), (ts, theta));
1311: PetscFunctionReturn(PETSC_SUCCESS);
1312: }
1314: /*@
1315: TSThetaSetTheta - Set the abscissa of the stage in (0,1] for `TSTHETA`
1317: Not Collective
1319: Input Parameters:
1320: + ts - timestepping context
1321: - theta - stage abscissa
1323: Options Database Key:
1324: . -ts_theta_theta <theta> - set theta
1326: Level: intermediate
1328: .seealso: [](ch_ts), `TSThetaGetTheta()`, `TSTHETA`, `TSCN`
1329: @*/
1330: PetscErrorCode TSThetaSetTheta(TS ts, PetscReal theta)
1331: {
1332: PetscFunctionBegin;
1334: PetscTryMethod(ts, "TSThetaSetTheta_C", (TS, PetscReal), (ts, theta));
1335: PetscFunctionReturn(PETSC_SUCCESS);
1336: }
1338: /*@
1339: TSThetaGetEndpoint - Gets whether to use the endpoint variant of the method (e.g. trapezoid/Crank-Nicolson instead of midpoint rule) for `TSTHETA`
1341: Not Collective
1343: Input Parameter:
1344: . ts - timestepping context
1346: Output Parameter:
1347: . endpoint - `PETSC_TRUE` when using the endpoint variant
1349: Level: advanced
1351: .seealso: [](ch_ts), `TSThetaSetEndpoint()`, `TSTHETA`, `TSCN`
1352: @*/
1353: PetscErrorCode TSThetaGetEndpoint(TS ts, PetscBool *endpoint)
1354: {
1355: PetscFunctionBegin;
1357: PetscAssertPointer(endpoint, 2);
1358: PetscUseMethod(ts, "TSThetaGetEndpoint_C", (TS, PetscBool *), (ts, endpoint));
1359: PetscFunctionReturn(PETSC_SUCCESS);
1360: }
1362: /*@
1363: TSThetaSetEndpoint - Sets whether to use the endpoint variant of the method (e.g. trapezoid/Crank-Nicolson instead of midpoint rule) for `TSTHETA`
1365: Not Collective
1367: Input Parameters:
1368: + ts - timestepping context
1369: - flg - `PETSC_TRUE` to use the endpoint variant
1371: Options Database Key:
1372: . -ts_theta_endpoint <flg> - use the endpoint variant
1374: Level: intermediate
1376: .seealso: [](ch_ts), `TSTHETA`, `TSCN`
1377: @*/
1378: PetscErrorCode TSThetaSetEndpoint(TS ts, PetscBool flg)
1379: {
1380: PetscFunctionBegin;
1382: PetscTryMethod(ts, "TSThetaSetEndpoint_C", (TS, PetscBool), (ts, flg));
1383: PetscFunctionReturn(PETSC_SUCCESS);
1384: }
1386: /*
1387: * TSBEULER and TSCN are straightforward specializations of TSTHETA.
1388: * The creation functions for these specializations are below.
1389: */
1391: static PetscErrorCode TSSetUp_BEuler(TS ts)
1392: {
1393: TS_Theta *th = (TS_Theta *)ts->data;
1395: PetscFunctionBegin;
1396: PetscCheck(th->Theta == 1.0, PetscObjectComm((PetscObject)ts), PETSC_ERR_OPT_OVERWRITE, "Can not change the default value (1) of theta when using backward Euler");
1397: PetscCheck(!th->endpoint, PetscObjectComm((PetscObject)ts), PETSC_ERR_OPT_OVERWRITE, "Can not change to the endpoint form of the Theta methods when using backward Euler");
1398: PetscCall(TSSetUp_Theta(ts));
1399: PetscFunctionReturn(PETSC_SUCCESS);
1400: }
1402: static PetscErrorCode TSView_BEuler(TS ts, PetscViewer viewer)
1403: {
1404: PetscFunctionBegin;
1405: PetscFunctionReturn(PETSC_SUCCESS);
1406: }
1408: /*MC
1409: TSBEULER - ODE solver using the implicit backward Euler method
1411: Level: beginner
1413: Note:
1414: `TSBEULER` is equivalent to `TSTHETA` with Theta=1.0 or `-ts_type theta -ts_theta_theta 1.0`
1416: .seealso: [](ch_ts), `TSCreate()`, `TS`, `TSSetType()`, `TSEULER`, `TSCN`, `TSTHETA`
1417: M*/
1418: PETSC_EXTERN PetscErrorCode TSCreate_BEuler(TS ts)
1419: {
1420: PetscFunctionBegin;
1421: PetscCall(TSCreate_Theta(ts));
1422: PetscCall(TSThetaSetTheta(ts, 1.0));
1423: PetscCall(TSThetaSetEndpoint(ts, PETSC_FALSE));
1424: ts->ops->setup = TSSetUp_BEuler;
1425: ts->ops->view = TSView_BEuler;
1426: PetscFunctionReturn(PETSC_SUCCESS);
1427: }
1429: static PetscErrorCode TSSetUp_CN(TS ts)
1430: {
1431: TS_Theta *th = (TS_Theta *)ts->data;
1433: PetscFunctionBegin;
1434: PetscCheck(th->Theta == 0.5, PetscObjectComm((PetscObject)ts), PETSC_ERR_OPT_OVERWRITE, "Can not change the default value (0.5) of theta when using Crank-Nicolson");
1435: PetscCheck(th->endpoint, PetscObjectComm((PetscObject)ts), PETSC_ERR_OPT_OVERWRITE, "Can not change to the midpoint form of the Theta methods when using Crank-Nicolson");
1436: PetscCall(TSSetUp_Theta(ts));
1437: PetscFunctionReturn(PETSC_SUCCESS);
1438: }
1440: static PetscErrorCode TSView_CN(TS ts, PetscViewer viewer)
1441: {
1442: PetscFunctionBegin;
1443: PetscFunctionReturn(PETSC_SUCCESS);
1444: }
1446: /*MC
1447: TSCN - ODE solver using the implicit Crank-Nicolson method.
1449: Level: beginner
1451: Notes:
1452: `TSCN` is equivalent to `TSTHETA` with Theta=0.5 and the "endpoint" option set. I.e.
1453: .vb
1454: -ts_type theta
1455: -ts_theta_theta 0.5
1456: -ts_theta_endpoint
1457: .ve
1459: .seealso: [](ch_ts), `TSCreate()`, `TS`, `TSSetType()`, `TSBEULER`, `TSTHETA`, `TSType`,
1460: M*/
1461: PETSC_EXTERN PetscErrorCode TSCreate_CN(TS ts)
1462: {
1463: PetscFunctionBegin;
1464: PetscCall(TSCreate_Theta(ts));
1465: PetscCall(TSThetaSetTheta(ts, 0.5));
1466: PetscCall(TSThetaSetEndpoint(ts, PETSC_TRUE));
1467: ts->ops->setup = TSSetUp_CN;
1468: ts->ops->view = TSView_CN;
1469: PetscFunctionReturn(PETSC_SUCCESS);
1470: }