wuwushrek / physics_constrained_nn
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
☆46Updated 3 years ago
Alternatives and similar repositories for physics_constrained_nn:
Users that are interested in physics_constrained_nn are comparing it to the libraries listed below
- A framework for neural network control of dynamical systems over graphs.☆57Updated 2 years ago
- IIB Master's Project: Deep Learning for Koopman Optimal Predictive Control☆44Updated 4 years ago
- ☆15Updated 4 years ago
- Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting☆42Updated last year
- Model-based Control using Koopman Operators☆51Updated 4 years ago
- AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.☆67Updated 9 months ago
- A Python package to learn the Koopman operator.☆53Updated 3 months ago
- Koopman Kernels for Learning Dynamical Systems from Trajectory Data☆24Updated last year
- Open-source implementation of Deep Lagrangian Networks (DeLaN)☆89Updated 2 months ago
- Official implementation for our paper "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"☆18Updated 2 years ago
- ☆44Updated 4 years ago
- This gym provides implementations of various PDEs for easy testing and comparison of data-driven and classical PDE control algorithms.☆19Updated last month
- Soure code for Deep Koopman with Control☆69Updated 2 years ago
- [ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control☆88Updated 3 years ago
- Source code for "Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control" fro…☆37Updated 5 years ago
- ☆65Updated 6 years ago
- Learning Lyapunov functions and control policies of nonlinear dynamical systems☆127Updated 3 years ago
- We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-infor…☆94Updated last year
- A general-purpose Python package for Koopman theory using deep learning.☆94Updated last week
- Koopman Reduced-Order Nonlinear Identification and Control☆86Updated 4 years ago
- High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, T…☆11Updated 2 weeks ago
- ☆83Updated 2 years ago
- We learn the dynamics model of a robot using a physics-informed neural network and use it to train a model-based RL algorithm.☆33Updated 8 months ago
- Consistent Koopman Autoencoders☆70Updated last year
- A Python package for linear subspace identification, nonlinear system identification, and nonlinear regression using Jax☆26Updated this week
- Companion code to "Learning Stable Deep Dynamics Models" (Manek and Kolter, 2019)☆32Updated 5 years ago
- "dynoNet: A neural network architecture for learning dynamical systems" by Marco Forgione and Dario Piga☆44Updated 8 months ago
- A Neural Network Approach for Real-Time High-Dimensional Optimal Control☆26Updated 2 years ago
- Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gy…☆134Updated last year
- Experiment code for "Continuous-Time Model-Based Reinforcement Learning"☆50Updated last year