Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
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Updated
Jul 20, 2024 - Python
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
torchbearer: A model fitting library for PyTorch
Hardware accelerated, batchable and differentiable optimizers in JAX.
Compositional Differentiable Programming Library
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Differentiable Finite Element Method with JAX
Robot kinematics implemented in pytorch
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.
Code for our NeurIPS 2022 paper
A differentiable bridge between phase space and Fock space
Differentiable Computational Lithogrpahy Framework
[ICLR 2021 top 3%] Is Attention Better Than Matrix Decomposition?
PyNeuraLogic lets you use Python to create Differentiable Logic Programs
Physics-driven machine learning using PyTorch and Firedrake
A JAX-based research framework for writing differentiable numerical simulators with arbitrary discretizations
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.
Differentiable and accelerated spherical transforms with JAX
dSGP4: differentiable SGP4. Supports differentiability, ML integration & embarassingly parallel computations
Hitchhiker's Guide to Deep Learning for Computational Scientists
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