A Python framework for accelerated simulation, data generation and spatial computing.
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Updated
Jun 12, 2025 - Python
A Python framework for accelerated simulation, data generation and spatial computing.
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Compositional Differentiable Programming Library
Hardware accelerated, batchable and differentiable optimizers in JAX.
torchbearer: A model fitting library for PyTorch
Robot kinematics implemented in pytorch
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Differentiable Finite Element Method with JAX
Code for our NeurIPS 2022 paper
[ICLR 2021 top 3%] Is Attention Better Than Matrix Decomposition?
Comprehensive optical design, optimization, and analysis in Python, including GPU-accelerated and differentiable ray tracing via PyTorch.
PyNeuraLogic lets you use Python to create Differentiable Logic Programs
S2FFT: Differentiable and accelerated spherical transforms
Differentiable Computational Lithogrpahy Framework
A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, control policy, or/and optimal trajectory in a control system.
A JAX-based research framework for writing differentiable numerical simulators with arbitrary discretizations
A differentiable bridge between phase space and Fock space
A domain-specific probabilistic programming language for reasoning about reasoning
Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad class of safety-critical learning and control tasks.
Autodiff-native, self-documenting software components for Simulation Intelligence. 📦
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