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Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

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For each adjoint method, it would be convenient to include a table specifying which are compatible with each kind of autodifferentiation.

documentation
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I'm trying to solve a mid-sized system of coupled differential equations with `diffrax`. I'm using version 0.2.0. Here's a short snippet of dummy code that raises the issue I'm having:...

question

Hi, I finally have the time to actually finish this PR. I've been trying to start from scratch instead of going over all of the changes, and currently the solvers...

Hi again 😃 I noticed that memory seems to grow indefinitely when integrating lots of newly defined functions. In the following example, memory grows at each iteration if `vector_field` is...

question

At the moment it uses `jnp.linalg.solve`, which assumes that the solve operation is well-posed. This needn't actually be the case, though, and at the moment the gradient we obtain could...

bug
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I try to use TPU on Colab with Diffrax. Error appears when I use TPU but no problem with CPU or GPU. ``` import jax.tools.colab_tpu from diffrax import diffeqsolve, Dopri5,...

question

One thing that needs thinking about is how this compares to the use of jump processes as a driving control, which morally speaking do something very similar. - [x] Discrete...

feature

At the moment, padding the state with constant values (zero vector field) means that `rms_norm(state) -> 0` as this padding increases. This can cause issues with steady state detection, and...

refactor

Hey @patrick-kidger , I recently started exploring diffrax (just realised how powerful diffrax is!, and kudos for the amazing work!). I was thinking if there was a way to solve...

question

In this case the size of the output array should be equal to the known-at-compile-time number of steps.

bug