xgxg1314,Learn2Learn,github

pdesbynns

This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means of neural networks using TensorFlow.

pecann

PECANNs: Physics and Equality Constrained Artificial Neural Networks

physics-based-deep-learning

Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

physics-informed-neural-networks

Investigating PINNs

physics-informed-neural-networks-for-power-systems

physics_informed_model_based_rl

We use physics-informed neural networks to train a model-based RL algorithm. We show that, in model-based RL, model accuracy mainly matters in environments that are sensitive to initial conditions.

picasso

Penalized Sparse Learning Solver - Unleash the Power of Nonconvex Penalty

pidoc

Physics-Informed Deep Operator Control (PIDOC), a deep learning method for controlling nonlinear chaos

pimbrl

Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control

pinn-laminar-flow

Physics-informed neural network for solving fluid dynamics problems

pinn_we

Discontinuity Computing Using Physics-Informed Neural Network

We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool to approximate (partial) differential equations. PINNs are not suited for control tasks in their original form since they are not designed to handle variable control actions or variable initial values. We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. The high-dimensional input space is subsequently reduced via a sampling strategy and a zero-hold assumption. This strategy enables the controller design based on a PINN as an approximation of the underlying system dynamics. The additional benefit is that the sensitivities are easily computed via automatic differentiation, thus leading to efficient gradient-based algorithms. Finally, we present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system, a multi-link manipulator.