{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Diffrax provides numerical solvers for ordinary, stochastic, and controlled differential equations in JAX, with automatic differentiation and GPU acceleration built in.","skillfed_tags":["jax-ecosystem","neural-odes","autodiff"],"use_cases":["Training neural differential equations where gradients flow through the solver during backpropagation.","Solving systems of ODEs/SDEs on GPU for large-scale scientific simulations with automatic differentiation.","Implementing controlled differential equations for sequence modeling and time-series tasks.","Research prototyping of dynamical systems where vmappable solvers enable batched integration over parameter ranges.","Combining differential equation solving with JAX's functional programming model in end-to-end differentiable pipelines."],"what_it_does":"Diffrax is a JAX-native library for solving differential equations\u2014ODEs, SDEs, and CDEs\u2014with full automatic differentiation and GPU support. It unifies the treatment of different equation types under a single internal architecture, making it compact and composable. The library is designed for researchers and practitioners working with neural differential equations, dynamical systems, and scientific computing where gradient-based optimization through the solver is needed.\n\nThe package integrates tightly with the JAX ecosystem (equinox, jaxtyping, lineax, optimistix) and supports advanced features like vmappable solvers, PyTree state representations, dense solutions, and multiple adjoint methods for backpropagation. It includes a range of solvers from standard choices like Dopri5 to specialized symplectic and implicit methods, all callable from a unified interface.","worth_installing":"Yes, if you work with differential equations in JAX or need autodiff through a solver. Low install friction, active maintenance, permissive license, and no known vulnerabilities. The Alpha status and tight coupling to the JAX ecosystem mean it's best suited for research and projects where you can tolerate API changes; for production systems requiring long-term stability, verify that the specific solver and adjoint method you need are stable."},"id":"diffrax","links":{"html":"https://skillfed.io/packages/diffrax","md":"https://skillfed.io/packages/diffrax.md","pypi":"https://pypi.org/project/diffrax/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-18","license_spdx":null,"license_treatment":"permissive","name":"diffrax","python_support":"supports_current","summary":"GPU+autodiff-capable ODE/SDE/CDE solvers written in JAX."},"popularity":{"monthly_downloads":357141,"position":7275,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.7.2"}