dalerxli Goto Github PK

pg_gan_metasurface

phasedarrayantennaanalysis

Python scripts to simulate and analyze phased array antennas

phaserecon

3D Phase contrast imaging reconstruction software

phd_pinns

philsol

Simple python library for calculating the modes of electromagnetic waveguides using finite difference frequency domain method.

photonflow

PhotonFlow: A design methodology and tool for design and simulation of photonic neural networks

photonic

Photonic and metamaterials calculations

photoniccrystal

The Photonic Crystal Bandgap

photoniccrystalsim

A simulation of an infinite two-dimensional photonic crystal with out-of-plane propagation using the plane wave expansion method

photonicsdevices

Micro/Nano Photonics devices and Inverse design

phyiscs-informed_reinforcement_learning

Novel Physics-informed approach to RL for physics-based control tasks

phys_categorizer

physics-based-deep-learning

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

physics-informed-deep-learning-solid-and-fluid-mechanics

Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems

physics-informed-deeponets

physics-informed-machine-learning

Artificial Neural Nets that incorporate Rigorous Coupled Wave Analysis into Loss Function

physics-informed-machine-learning-for-qnms

Computes the QNMs of black holes, employing deep neural networks with physics constraints embedded in the loss function. This code is a customises the code originally created by https://github.com/henry1jin/eigeNN.

physics-informed-neural-networks

Investigating PINNs

physics-informed-neural-networks-pytorch

Implementation of physics informed neural networks with PyTorch

physics_informed_neural_networks

Physics Informed Neural Networks

physicsinformedneuralnetwork

Some Background work for my thesis in PINNs

pina

Physics-Informed Neural networks for Advanced modeling

pinn

Physics-informed neural networks package

pinn---physics-informed-neural-networks

Physics Informed Neural Networks (PINN) are neural networks, where a library of variables and derivatives are believed to form a PDE which describes the data. The system then seeks a neural network which arrives at a system that replicates a data set.

pinn-1

codes for PINNs

pinn-2

Simple PyTorch Implementation of Physics Informed Neural Network (PINN)

pinn-3

pinn-4

pinn-5

2nd Order Wave Equation PINN Solution/ TensorFlow & PyTorch

pinn-a-dnn-pde-solver

Physics Informed Neural Network for Solving Partial Differential Equation (In processing)