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Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
Python helpers to limit the number of threads used in native libraries that handle their own internal threadpool (BLAS and OpenMP implementations)
A new markup-based typesetting system that is powerful and easy to learn.
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
Large-scale prior fields based on the SPDE approach to Matérn fields
Non-parametric Bayesian Inference for Stochastic Processes
A light-weight numerical integrator for stochastic differential equations
The fundamental package for scientific computing with Python.
NumPy aware dynamic Python compiler using LLVM
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Unbearably fast near-real-time hybrid runtime-static type-checking in pure Python.
Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. https://docs.kidger.site/jaxtyping/
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…
Grid-based approximation of partial differential equations in Julia
Github Action to: Check version / Test / git tag / GitHub Release / Deploy to PyPI
Fast low-rank matrix approximation in Julia
A light-weight library for large-scale Markov Chain Monte Carlo sampling
UM-Bridge (the UQ and Model Bridge) provides a unified interface for numerical models that is accessible from virtually any programming language or framework.
A Fully Differentiable Solver for the Anisotropic Eikonal Equation