A Python framework for GPU-accelerated simulation, robotics, and machine learning.
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.