# ODE-Solvers from Scratch¶

All the other tutorials show how to use the ODE-solver with the `probsolve_ivp`

function. This is great, though `probnum`

has more customisation to offer.

```
[1]:
```

```
from probnum import diffeq, filtsmooth, statespace, randvars, randprocs, problems
import numpy as np
import matplotlib.pyplot as plt
plt.style.use("../../probnum.mplstyle")
```

First we define the ODE problem. As always, we use Lotka-Volterra. Once the ODE functions are defined, they are gathered in an `IVP`

object.

```
[2]:
```

```
def f(t, y):
y1, y2 = y
return np.array([0.5 * y1 - 0.05 * y1 * y2, -0.5 * y2 + 0.05 * y1 * y2])
def df(t, y):
y1, y2 = y
return np.array([[0.5 - 0.05 * y2, -0.05 * y1], [0.05 * y2, -0.5 + 0.05 * y1]])
t0 = 0.0
tmax = 20.0
y0 = np.array([20, 20])
ivp = problems.InitialValueProblem(t0=t0, tmax=tmax, y0=y0, f=f, df=df)
```

Next, we define a prior distribution and a measurement model. The former can be any `Integrator`

, which currently restricts the choice to `IBM`

, `IOUP`

, and `Matern`

. We usually recommend `IBM`

. The measurement model requires a choice between EK0, EK1 (extended Kalman filters of order 0 or 1, respectively) and perhaps UK (unscented Kalman filter). The use of the latter is discouraged, because the square-root implementation is not available currently.

The measurement model can either be constructed with `DiscreteEKFComponent.from_ode`

or, perhaps more conveniently, with `GaussianIVPFilter.string_to_measurement_model`

.

```
[3]:
```

```
prior = statespace.IBM(
ordint=4,
spatialdim=ivp.dimension,
forward_implementation="sqrt",
backward_implementation="sqrt",
)
initrv = randvars.Normal(mean=np.zeros(prior.dimension), cov=np.eye(prior.dimension))
prior_process = randprocs.MarkovProcess(transition=prior, initrv=initrv, initarg=ivp.t0)
ekf = diffeq.GaussianIVPFilter.string_to_measurement_model(
"EK1", ivp=ivp, prior_process=prior_process
)
```

Next, we construct the ODE filter. One choice that has not been made yet is the initialiation strategy. The current default choice is to initialise by fitting the prior to a few steps of a Runge-Kutta solution. An alternative is to use automatic differentiation, which is currently in development. An easy-access version of those initialisation strategies is to use the constructor `GaussianIVPFilter.construct_with_rk_init`

.

```
[4]:
```

```
diffmodel =statespace.PiecewiseConstantDiffusion(t0=t0)
solver = diffeq.GaussianIVPFilter.construct_with_rk_init(ivp, prior_process=prior_process, measurement_model=ekf, diffusion_model=diffmodel, with_smoothing=True)
```

Now we can solve the ODE. To this end, define a `StepRule`

, e.g. `ConstantSteps`

or `AdaptiveSteps`

. If you don’t know which firststep to use, the function `propose_firststep`

makes an educated guess for you.

```
[5]:
```

```
firststep = diffeq.propose_firststep(ivp)
steprule = diffeq.AdaptiveSteps(firststep=firststep, atol=1e-3, rtol=1e-5)
# steprule = diffeq.ConstantSteps(0.1)
odesol = solver.solve(steprule=steprule)
```

`GaussianIVPFilter.solve`

returns an `ODESolution`

object, which is sliceable and callable. The latter can be used to plot the solution on a uniform grid, even though the solution was computed on an adaptive grid. Be careful: the return values of `__call__`

, etc., are always random variable-like objects. We decide to plot the mean.

```
[6]:
```

```
evalgrid = np.arange(ivp.t0, ivp.tmax, step=0.1)
```

Done! This is the solution to the Lotka-Volterra model.

```
[7]:
```

```
sol = odesol(evalgrid)
plt.plot(evalgrid, sol.mean, "o-", linewidth=1)
plt.ylim((0, 30))
plt.show()
```

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