jaem-seo / pinn-optimizationLinks
☆27Updated last year
Alternatives and similar repositories for pinn-optimization
Users that are interested in pinn-optimization are comparing it to the libraries listed below
Sorting:
- Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling☆52Updated 3 years ago
- Open-source implementation of Deep Lagrangian Networks (DeLaN)☆125Updated 10 months 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.☆45Updated last year
- High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, T…☆13Updated 8 months ago
- Code for "SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning" by Zolman et al.☆137Updated last month
- A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, contr…☆178Updated 2 years ago
- Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad…☆71Updated 3 years ago
- Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gy…☆145Updated 2 years ago
- Synthesizing neural-network Lyapunov functions (and controllers) as stability certificate.☆168Updated 4 months ago
- Automatic Tuning for Data-driven Model Predictive Control☆68Updated 2 years ago
- Soure code for Deep Koopman with Control☆90Updated 3 years ago
- We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-infor…☆122Updated last year
- Model-based Control using Koopman Operators☆52Updated 5 years ago
- [NeurIPS 2023] Neural Lyapunov Control for Discrete-Time Systems☆12Updated last year
- Use deep learning to learn Koopman operator and LQR for optimal control☆16Updated 5 years ago
- HILO-MPC is a Python toolbox for easy, flexible and fast development of machine-learning-supported optimal control and estimation problem…☆184Updated this week
- Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control☆56Updated 3 years ago
- MISO: Learning Multiple Initial Solutions to Optimization Problems☆15Updated 11 months ago
- ☆14Updated 8 months ago
- Hamilton-Jacobi reachability analysis in JAX.☆151Updated 11 months ago
- Safe robot learning☆97Updated 10 months ago
- deepkoopman的实现☆12Updated 2 years ago
- AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.☆78Updated last year
- DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control☆23Updated 5 years ago
- A framework for neural network control of dynamical systems over graphs.☆56Updated 3 years ago
- ☆26Updated 3 years ago
- Code for Paper "Gradient Informed Proximal Policy Optimization" (NeurIPS 2023)☆26Updated last year
- A python library for control from Signal Temporal Logic (STL) specifications☆47Updated 4 months ago
- Learning Lyapunov functions and control policies of nonlinear dynamical systems☆138Updated 4 years ago
- ☆63Updated last year