# Differential-Algebraic Equations

Stan support solving systems of differential-algebraic equations (DAEs) of index 1 . The solver adaptively refines the solutions in order to satisfy given tolerances.

One can think a differential-algebraic system of equations as ODEs with additional algebraic constraints applied to some of the variables. In such a system, the variable derivatives may not be expressed explicitly with a right-hand-side as in ODEs, but implicitly constrained.

Similar to ODE solvers, the DAE solvers must not only provide the solution to the DAE itself, but also the gradient of the DAE solution with respect to parameters (the sensitivities). Stan’s DAE solver uses the forward sensitivity calculation to expand the base DAE system with additional DAE equations for the gradients of the solution. For each parameter, an additional full set of $$N$$ sensitivity states are added meaning that the full DAE solved has $$N \, + N \cdot M$$ states.

Two interfaces are provided for the forward sensitivity solver: one with default tolerances and default max number of steps, and one that allows these controls to be modified. Choosing tolerances is important for making any of the solvers work well – the defaults will not work everywhere. The tolerances should be chosen primarily with consideration to the scales of the solutions, the accuracy needed for the solutions, and how the solutions are used in the model. The same principles in the control parameters section apply here.

Internally Stan’s DAE solver uses a variable-step, variable-order, backward-differentiation formula implementation .

## Notation

A DAE is defined by a set of expressions for the residuals of differential equations and algebraic equations $$r(y', y, t, \theta)$$, and consistent initial conditions $$y(t_0, \theta) = y_0, y'(t_0, \theta)=y'_0$$. The DAE is define by residual function as $$r(y', y, t, \theta)=0$$. The $$\theta$$ dependence is included in the notation to highlight that the solution $$y(t)$$ is a function of any parameters used in the computation.

## Example: chemical kinetics

As an example of a system of DAEs, consider following chemical kinetics problem. The nondimensionalized DAE consists of two differential equations and one algebraic constraint. The differential equations describe the reactions from reactants $$y_1$$ and $$y_2$$ to the product $$y_3$$, and the algebraic equation describes the mass conservation. .

\begin{align*} \frac{dy_1}{dt} + \alpha y_1 - \beta y_2 y_3 = 0 \\ \frac{dy_2}{dt} - \alpha y_1 + \beta y_2 y_3 + \gamma y_2^2 = 0 \\ y_1 + y_2 + y_3 - 1.0 = 0 \end{align*}

The state equations implicitly defines the state $$(y_1(t), y_2(t), y_3(t))$$ at future times as a function of an initial state and the system parameters, in this example the reaction rate coefficients $$(\alpha, \beta, \gamma)$$.

Unlike solving ODEs, solving DAEs requires a consistent initial condition. That is, one must specify both $$y(t_0)$$ and $$y'(t_0)$$ so that residual function becomes zero at initial time $$t_0$$ $\begin{equation*} r(y'(t_0), y(t_0), t_0) = 0 \end{equation*}$

## Index of DAEs

The index along a DAE solution $$y(t)$$ is the minimum number of differentiations of some of the components of the system required to solve for $$y'$$ uniquely in terms of $$y$$ and $$t$$, so that the DAE is converted into an ODE for $$y$$. Thus an ODE system is of index 0. The above chemical kinetics DAE is of index 1, as we can perform differentiation of the third equation followed by introducing the first two equations in order to obtain the ODE for $$y_3$$.

Most DAE solvers, including the one in Stan, support only index-1 DAEs. For a high index DAE problem the user must first convert it to a lower index system. This often can be done by carrying out differentiations analytically .

## Coding the DAE system function

The first step in coding an DAE system in Stan is defining the DAE residual function. The system functions require a specific signature so that the solvers know how to use them properly.

The first argument to the residual function is time, passed as a real; the second argument to the residual function is the system state $$y$$, passed as a vector, the third argument to the residual function is the state derivative $$y'$$, also passed as a vector. The residual function’s return value is a vector of the same size as state and state derivatives. Additional arguments can be included in the residual function to pass other information into the solve (these will be passed through the function that starts the DAE solution). These argument can be parameters (in our example, the reaction rate coefficient $$\alpha$$, $$\beta$$, and $$\gamma$$), data, or any quantities that are needed to define the DAE.

The above reaction be coded using the following function in Stan (see the user-defined functions chapter for more information on coding user-defined functions).

 vector chem(real t, vector yy, vector yp,
real alpha, real beta, real gamma) {
vector[3] res;
res[1] = yp[1] + alpha * yy[1] - beta * yy[2] * yy[3];
res[2] = yp[2] - alpha * yy[1] + beta * yy[2] * yy[3] + gamma * yy[2] * yy[2];
res[3] = yy[1] + yy[2] + yy[3] - 1.0;
return res;
}
}

The function takes in a time t (a real), the system state yy (a vector), state derivative yp (a vector), as well as parameter alpha (a real), beta (a real), and gamma (a real). The function returns a vector of the residuals at time t. The DAE coded here does not explicitly depend on t, however one still needs to specify t as an argument.

### Strict signature

The types in the DAE residual function are strict. The first argument is the time passed as a real, the second argument is the state passed as a vector, the third argument is the state derivative passed as a vector, and the return type is a vector. A model that does not have this signature will fail to compile. The fourth argument onwards can be any type, granted all the argument types match the types of the respective arguments in the solver call.

All of these are possible DAE signatures:

vector my_dae1(real t, vector y, vector yp, real a0);
vector my_dae2(real t, vector y, vector yp, array[] int a0, vector a1);
vector my_dae3(real t, vector y, vector yp, matrix a0, array[] real a1, row_vector a2);

but these are not allowed:

vector my_dae1(real t, array[] real y, vector yp);
// Second argument is not a vector
array[] real my_dae2(real t, vector y, vector yp);
// Return type is not a vector
vector my_dae3(real t, vector y);
// First argument is not a real and missing the third argument

## Solving DAEs

Stan provides a dae function for solving DAEs, so that the above chemical reaction equation can be solved in the following code.

data {
int N;
vector[3] yy0;
vector[3] yp0;
real t0;
real alpha;
real beta;
array[N] real ts;
array[N] vector[3] y;
}
parameters {
real gamma;
}
transformed parameters {
vector[3] y_hat[N] = dae(chem, yy0, yp0, t0, ts, alpha, beta, gamma);
}

Since gamma is a parameter, the DAE solver is called in the transformed parameters block.

## Control parameters for DAE solving

Using dae_tol one can specify the relative_tolerance, absolute_tolerance, and max_num_steps parameters in order to control the DAE solution.

vector[3] y_hat[N] = dae_tol(chem, yy0, yp0, t0, ts,
relative_tolerance,
absolute_tolerance,
max_num_steps,
alpha, beta, gamma);

relative_tolerance and absolute_tolerance control accuracy the solver tries to achieve, and max_num_steps specifies the maximum number of steps the solver will take between output time points before throwing an error.

The control parameters must be data variables – they cannot be parameters or expressions that depend on parameters, including local variables in any block other than transformed data and generated quantities. User-defined function arguments may be qualified as only allowing data arguments using the data qualifier.

The default value of relative and absolute tolerances are $$10^{-10}$$ and the maximum number of steps between outputs is one hundred million. We suggest the user choose the control parameters according to the problem in hand, and resort to the defaults only when no knowledge of the DAE system or the physics it models is available.

### Maximum number of steps

The maximum number of steps can be used to stop a runaway simulation. This can arise in when MCMC moves to a part of parameter space very far from where a differential equation would typically be solved. In particular this can happen during warmup. With the non-stiff solver, this may happen when the sampler moves to stiff regions of parameter space, which will requires small step sizes.