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diffrax
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
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This is a pretty standard thing to do. Probably the easiest place to start is optimising the parameters by gradient descent, for which you'll want autodifferentiable diffeq solver. In Python that means a tool like Diffrax.
Haha! Yep, this is mine. I believe this is the standard tool for this job in the JAX ecosystem, so I wouldn't normally add the disclaimer. FWIW the only other mainstream Python tool here is torchdiffeq and I wrote a fair chunk of that package too so ¯\(ツ)/¯
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