Diffrax
A JAX-based library providing numerical differential equation solvers for ODEs, SDEs, and CDEs with autodifferentiation and GPU support.
What is Diffrax?
Diffrax is a numerical differential equation solving library built on JAX, designed for scientific computing and machine learning applications. It provides a unified approach to solving ordinary, stochastic, and controlled differential equations with support for autodifferentiation and GPU acceleration.
Researchers and engineers in scientific computing and machine learning who need to solve differential equations within the JAX ecosystem, particularly those working with neural differential equations or requiring efficient backpropagation through ODEs/SDEs/CDEs.
Developers choose Diffrax for its unified internal architecture that consistently handles different equation types, its seamless integration with JAX's functional programming model via PyTree state, and its performance features like vmappable integration and multiple adjoint methods for backpropagation.
Overview
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
Use Cases
Best For
- Solving ordinary differential equations (ODEs) with autodifferentiation and GPU support in JAX.
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