Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
MIT License
A common interface for quadrature and numerical integration for the SciML scientific machine lear...
A benchmarking framework for the Julia language
Multi-language suite for high-performance solvers of differential equations and scientific machin...
Cross-architecture parallel algorithms for Julia's GPU backends, from a unified KernelAbstraction...
Performance and data profiles
Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia
Provides a platform for the Julia community to compare AI models' abilities in generating syntact...
Unitary and Lindbladian evolution in Julia
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) ...
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) s...
🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
Wrappers for the SciPy differential equation solvers for the SciML Scientific Machine Learning or...
Tutorials for doing scientific machine learning (SciML) and high-performance differential equatio...
Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Qu...