Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
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Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
Optax is a gradient processing and optimization library for JAX.
Solve automatic numerical differentiation problems in one or more variables.
DX Analytics | Financial and Derivatives Analytics with Python
