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I work in scientific machine learning: combining deep learning with differential equations.

A few projects I'm proud of:

  • Differential equation solvers in JAX: Diffrax;
  • Differential equation solvers in PyTorch: torchsde, torchcde;
  • Tensor type annotations (inc. shape, dtypes, ...) for JAX/NumPy/PyTorch: jaxtyping;
  • Neural networks in JAX, elegantly: Equinox.

See also:

Please feel free to use my work without sponsorship! A few kind souls asked for this as a way to say thanks :)

Current sponsors 2

@Artur-Galstyan
@dlwh
Past sponsors 12
Private Sponsor
@conceptofmind
@madisonmay
@gabrielmbmb
@alvarobartt
Private Sponsor
@lucidrains
@hongyehu
@fkodom
@arf-themascoteers
@sequoiar
@taskswithcode

Featured work

  1. google-research/torchsde

    Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

    Python 1,580
  2. patrick-kidger/diffrax

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

    Python 1,431
  3. patrick-kidger/torchcde

    Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.

    Python 420
  4. patrick-kidger/equinox

    Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/

    Python 2,104
  5. patrick-kidger/torchtyping

    Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.

    Python 1,403

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