Productive, portable, and performant GPU programming in Python.
A Python framework for GPU-accelerated simulation, robotics, and machine learning.
High-performance automatic differentiation of LLVM and MLIR.
Fast and embedded solvers for nonlinear optimal control and nonlinear model predictive control
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Hardware accelerated, batchable and differentiable optimizers in JAX.
Comprehensive optical design, optimization, and analysis in Python, including GPU-accelerated and differentiable ray tracing via PyTorch.
Differentiable Finite Element Method with JAX
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Julia bindings for the Enzyme automatic differentiator