petsc-3.15.0 2021-03-30
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# Performing sensitivity analysis

The `TS` library provides a framework based on discrete adjoint models for sensitivity analysis for ODEs and DAEs. The ODE/DAE solution process (henceforth called the forward run) can be obtained by using either explicit or implicit solvers in `TS`, depending on the problem properties. Currently supported method types are `TSRK` (Runge-Kutta) explicit methods and `TSTHETA` implicit methods, which include `TSBEULER` and `TSCN`.

## Using the discrete adjoint methods

Consider the ODE/DAE

\[F(t,y,\dot{y},p) = 0, \quad y(t_0)=y_0(p) \quad t_0 \le t \le t_F \]

and the cost function(s)

\[\Psi_i(y_0,p) = \Phi_i(y_F,p) + \int_{t_0}^{t_F} r_i(y(t),p,t)dt \quad i=1,...,n_\text{cost}. \]

The `TSAdjoint` routines of PETSc provide

\[\frac{\partial \Psi_i}{\partial y_0} = \lambda_i \]

and

\[\frac{\partial \Psi_i}{\partial p} = \mu_i + \lambda_i (\frac{\partial y_0}{\partial p}). \]

To perform the discrete adjoint sensitivity analysis one first sets up the `TS` object for a regular forward run but with one extra function call

    TSSetSaveTrajectory(TS ts),

then calls `TSSolve()` in the usual manner.

One must create two arrays of \(n_\text{cost}\) vectors \(\lambda\) and\(\mu\) (if there are no parameters \(p\) then one can use `NULL` for the \(\mu\) array.) The \(\lambda\) vectors are the same dimension and parallel layout as the solution vector for the ODE, the \(mu\) vectors are of dimension \(p\); when \(p\) is small usually all its elements are on the first MPI process, while the vectors have no entries on the other processes. \(\lambda_i\) and \(mu_i\) should be initialized with the values \(d\Phi_i/dy|_{t=t_F}\) and \(d\Phi_i/dp|_{t=t_F}\) respectively. Then one calls

    TSSetCostGradients(TS ts,PetscInt numcost, Vec *lambda,Vec *mu);

If \(F()\) is a function of \(p\) one needs to also provide the Jacobian \(-F_p\) with

    TSSetRHSJacobianP(TS ts,Mat Amat,PetscErrorCode (*fp)(TS,PetscReal,Vec,Mat,void*),void *ctx)

The arguments for the function `fp()` are the timestep context, current time, \(y\), and the (optional) user-provided context.

If there is an integral term in the cost function, i.e. \(r\) is nonzero, it can be transformed into another ODE that is augmented to the original ODE. To evaluate the integral, one needs to create a child `TS` objective by calling

    TSCreateQuadratureTS(TS ts,PetscBool fwd,TS *quadts);

and provide the ODE RHS function (which evaluates the integrand \(r\)) with

    TSSetRHSFunction(TS quadts,Vec R,PetscErrorCode (*rf)(TS,PetscReal,Vec,Vec,void*),void *ctx)

Similar to the settings for the original ODE, Jacobians of the integrand can be provided with

    TSSetRHSJacobian(TS quadts,Vec DRDU,Vec DRDU,PetscErrorCode (*drdyf)(TS,PetscReal,Vec,Vec*,void*),void *ctx)
    TSSetRHSJacobianP(TS quadts,Vec DRDU,Vec DRDU,PetscErrorCode (*drdyp)(TS,PetscReal,Vec,Vec*,void*),void *ctx)

where \(\mathrm{drdyf}= dr /dy\), \(\mathrm{drdpf} = dr /dp\). Since the integral term is additive to the cost function, its gradient information will be included in \(\lambda\) and \(\mu\).

Lastly, one starts the backward run by calling

    TSAdjointSolve(TS ts).

One can obtain the value of the integral term by calling

    TSGetCostIntegral(TS ts,Vec *q).

or accessing directly the solution vector used by quadts.

The second argument of `TSCreateQuadratureTS()` allows one to choose if the integral term is evaluated in the forward run (inside `TSSolve()`) or in the backward run (inside `TSAdjointSolve()`) when `TSSetCostGradients()` and `TSSetCostIntegrand()` are called before `TSSolve()`. Note that this also allows for evaluating the integral without having to use the adjoint solvers.

To provide a better understanding of the use of the adjoint solvers, we introduce a simple example, corresponding to TS Power Grid Tutorial ex3adj. The problem is to study dynamic security of power system when there are credible contingencies such as short-circuits or loss of generators, transmission lines, or loads. The dynamic security constraints are incorporated as equality constraints in the form of discretized differential equations and inequality constraints for bounds on the trajectory. The governing ODE system is

\[\begin{aligned} \phi' &= &\omega_B (\omega - \omega_S) \\ 2H/\omega_S \, \omega' & =& p_m - p_{max} sin(\phi) -D (\omega - \omega_S), \quad t_0 \leq t \leq t_F,\end{aligned}\]

where \(\phi\) is the phase angle and \(\omega\) is the frequency.

The initial conditions at time \(t_0\) are

\[\begin{aligned} \phi(t_0) &=& \arcsin \left( p_m / p_{max} \right), \\ w(t_0) & =& 1.\end{aligned}\]

\(p_{max}\) is a positive number when the system operates normally. At an event such as fault incidence/removal, \(p_{max}\) will change to \(0\) temporarily and back to the original value after the fault is fixed. The objective is to maximize \(p_m\) subject to the above ODE constraints and \(\phi<\phi_S\) during all times. To accommodate the inequality constraint, we want to compute the sensitivity of the cost function

\[\Psi(p_m,\phi) = -p_m + c \int_{t_0}^{t_F} \left( \max(0, \phi - \phi_S ) \right)^2 dt \]

with respect to the parameter \(p_m\). \(numcost\) is \(1\) since it is a scalar function.

For ODE solution, PETSc requires user-provided functions to evaluate the system \(F(t,y,\dot{y},p)\) (set by `TSSetIFunction()` ) and its corresponding Jacobian \(F_y + \sigma F_{\dot y}\) (set by `TSSetIJacobian()`). Note that the solution state \(y\) is \([ \phi \; \omega ]^T\) here. For sensitivity analysis, we need to provide a routine to compute \(\mathrm{f}_p=[0 \; 1]^T\) using `TSASetRHSJacobianP()`, and three routines corresponding to the integrand \(r=c \left( \max(0, \phi - \phi_S ) \right)^2\), \(r_p = [0 \; 0]^T\) and \(r_y= [ 2 c \left( \max(0, \phi - \phi_S ) \right) \; 0]^T\) using `TSSetCostIntegrand()`.

In the adjoint run, \(\lambda\) and \(\mu\) are initialized as \([ 0 \; 0 ]^T\) and \([-1]\) at the final time \(t_F\). After `TSAdjointSolve()`, the sensitivity of the cost function w.r.t. initial conditions is given by the sensitivity variable \(\lambda\) (at time \(t_0\)) directly. And the sensitivity of the cost function w.r.t. the parameter \(p_m\) can be computed (by users) as

\[\frac{\mathrm{d} \Psi}{\mathrm{d} p_m} = \mu(t_0) + \lambda(t_0) \frac{\mathrm{d} \left[ \phi(t_0) \; \omega(t_0) \right]^T}{\mathrm{d} p_m} . \]

For explicit methods where one does not need to provide the Jacobian \(F_u\) for the forward solve one still does need it for the backward solve and thus must call

    TSSetRHSJacobian(TS ts,Mat Amat, Mat Pmat,PetscErrorCode (*f)(TS,PetscReal,Vec,Mat,Mat,void*),void *fP);

Examples include:

- a discrete adjoint sensitivity using explicit time stepping methods TS Tutorial ex16adj,

- a discrete adjoint sensitivity using implicit time stepping methods TS Tutorial ex20adj,

- an optimization using the discrete adjoint models of ERK TS Tutorial ex16opt_ic and TS Tutorial ex16opt_p <https://www.mcs.anl.gov/petsc/petsc-current/src/ts/tutorials/ex16opt_p.c.html>`__,

- an optimization using the discrete adjoint models of Theta methods for stiff DAEs TS Tutorial ex20opt_ic and TS Tutorial ex20opt_p,

- an ODE-constrained optimization using the discrete adjoint models of Theta methods for cost function with an integral term TS Power Grid Tutorial ex3opt,

- a discrete adjoint sensitivity using `TSCN` (Crank-Nicolson) methods for DAEs with discontinuities TS Power Grid Stability Tutorial ex9busadj.c,

- a DAE-constrained optimization using the discrete adjoint models of `TSCN` (Crank-Nicolson) methods for cost function with an integral term TS Power Grid Tutorial ex9busopt.c,

- a discrete adjoint sensitivity using `TSCN` methods for a PDE problem TS Advection-Diffusion-Reaction Tutorial ex5adj.

## Checkpointing

The discrete adjoint model requires the states (and stage values in the context of multistage timestepping methods) to evaluate the Jacobian matrices during the adjoint (backward) run. By default, PETSc stores the whole trajectory to disk as binary files, each of which contains the information for a single time step including state, time, and stage values (optional). One can also make PETSc store the trajectory to memory with the option `-ts_trajectory_type memory`. However, there might not be sufficient memory capacity especially for large-scale problems and long-time integration.

A so-called checkpointing scheme is needed to solve this problem. The scheme stores checkpoints at selective time steps and recomputes the missing information. The `revolve` library is used by PETSc `TSTrajectory` to generate an optimal checkpointing schedule that minimizes the recomputations given a limited number of available checkpoints. One can specify the number of available checkpoints with the option `-ts_trajectory_max_cps_ram [maximum number of checkpoints in RAM]`. Note that one checkpoint corresponds to one time step.

The `revolve` library also provides an optimal multistage checkpointing scheme that uses both RAM and disk for storage. This scheme is automatically chosen if one uses both the option `-ts_trajectory_max_cps_ram [maximum number of checkpoints in RAM]` and the option `-ts_trajectory_max_cps_disk [maximum number of checkpoints on disk]`.

Some other useful options are listed below.

- `-ts_trajectory_view` prints the total number of recomputations,

- `-ts_monitor` and `-ts_adjoint_monitor` allow users to monitor the progress of the adjoint work flow,

- `-ts_trajectory_type visualization` may be used to save the whole trajectory for visualization. It stores the solution and the time, but no stage values. The binary files generated can be read into MATLAB via the script `$PETSC_DIR/share/petsc/matlab/PetscReadBinaryTrajectory.m`.

# Solving Steady-State Problems with Pseudo-Timestepping

Simple Example: `TS` provides a general code for performing pseudo timestepping with a variable timestep at each physical node point. For example, instead of directly attacking the steady-state problem

\[G(u) = 0, \]

we can use pseudo-transient continuation by solving

\[u_t = G(u). \]

Using time differencing

\[u_t \doteq \frac{{u^{n+1}} - {u^{n}} }{dt^{n}} \]

with the backward Euler method, we obtain nonlinear equations at a series of pseudo-timesteps

\[\frac{1}{dt^n} B (u^{n+1} - u^{n} ) = G(u^{n+1}). \]

For this problem the user must provide \(G(u)\), the time steps \(dt^{n}\) and the left-hand-side matrix \(B\) (or optionally, if the timestep is position independent and \(B\) is the identity matrix, a scalar timestep), as well as optionally the Jacobian of \(G(u)\).

More generally, this can be applied to implicit ODE and DAE for which the transient form is

\[F(u,\dot{u}) = 0. \]

For solving steady-state problems with pseudo-timestepping one proceeds as follows.

- Provide the function `G(u)` with the routine

      TSSetRHSFunction(TS ts,Vec r,PetscErrorCode (*f)(TS,PetscReal,Vec,Vec,void*),void *fP);

  The arguments to the function `f()` are the timestep context, the current time, the input for the function, the output for the function and the (optional) user-provided context variable `fP`.

- Provide the (approximate) Jacobian matrix of `G(u)` and a function to compute it at each Newton iteration. This is done with the command

      TSSetRHSJacobian(TS ts,Mat Amat, Mat Pmat,PetscErrorCode (*f)(TS,PetscReal,Vec,Mat,Mat,void*),void *fP);

  The arguments for the function `f()` are the timestep context, the current time, the location where the Jacobian is to be computed, the (approximate) Jacobian matrix, an alternative approximate Jacobian matrix used to construct the preconditioner, and the optional user-provided context, passed in as `fP`. The user must provide the Jacobian as a matrix; thus, if using a matrix-free approach, one must create a `MATSHELL` matrix.

In addition, the user must provide a routine that computes the pseudo-timestep. This is slightly different depending on if one is using a constant timestep over the entire grid, or it varies with location.

- For location-independent pseudo-timestepping, one uses the routine

      TSPseudoSetTimeStep(TS ts,PetscInt(*dt)(TS,PetscReal*,void*),void* dtctx);

  The function `dt` is a user-provided function that computes the next pseudo-timestep. As a default one can use `TSPseudoTimeStepDefault(TS,PetscReal*,void*)` for `dt`. This routine updates the pseudo-timestep with one of two strategies: the default

  \[dt^{n} = dt_{\mathrm{increment}}*dt^{n-1}*\frac{|| F(u^{n-1}) ||}{|| F(u^{n})||} \]

  or, the alternative,

  \[dt^{n} = dt_{\mathrm{increment}}*dt^{0}*\frac{|| F(u^{0}) ||}{|| F(u^{n})||} \]

  which can be set with the call

      TSPseudoIncrementDtFromInitialDt(TS ts);

  or the option `-ts_pseudo_increment_dt_from_initial_dt`. The value \(dt_{\mathrm{increment}}\) is by default \(1.1\), but can be reset with the call

      TSPseudoSetTimeStepIncrement(TS ts,PetscReal inc);

  or the option `-ts_pseudo_increment <inc>`.

- For location-dependent pseudo-timestepping, the interface function has not yet been created.

TS: Scalable ODE and DAE Solvers High Level Support for Multigrid with KSPSetDM() and SNESSetDM()