uwknight's Stars
ratnania/mlhiphy
Machine Learning for Hidden Physics and Partial Differential Equations
Jonas-Nicodemus/PINNs-based-MPC
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.
AMReX-Codes/amrex
AMReX: Software Framework for Block Structured AMR
Tecplot/handyscripts
Useful PyTecplot and Tecplot Macro Scripts.
loliverhennigh/Steady-State-Flow-With-Neural-Nets
A Tensorflow re-implementation of the paper Convolutional Neural Networks for Steady Flow Approximation
duncanam/rhoReactingCentralFoam
An OpenFOAM solver with AMR that combines rhoCentralFoam and rhoReactingFoam for high-speed reactive flows.
synthetik-technologies/blastfoam
A CFD solver for multi-component compressible flow with application to high-explosive detonation, explosive safety and air blast
clawpack/amrclaw
AMR version of Clawpack