Pinned Repositories
DeepINN
A Physics-informed neural network (PINN) library.
DeepXDE-frontend
A GUI frontend for DeepXDE.
Discontinuous-PINN
A PINN implementation to automate the solution of discontinuous problems
Dynamics-Earthquake-Analysis-of-Structures
The main objectives of this individual project are: 1) to further enhance the understanding of the numerical time integration method – Newmark’s algorithm by numerically investigating its accuracy and stability, and by implementing it using a computer programming language (e.g. MatLab, C, C++, or Python); 2) to generate the earthquake response spectra from a particular earthquake; and 3) to undertake an earthquake analysis of a simple frame structure.
Nvidia-modulus-legacy-docs
NVIDIA modulus legacy docs
PDE-sparse-identification
Inspired from PDE-FIND
PINNpapers
Must-read Papers on Physics-Informed Neural Networks.
Sparse-System-Identification
This work presents the application of machine learning models in order to obtain a sparse governing equation of complex fluid dynamics problems.
Stiff-PDEs-and-Physics-Informed-Neural-Networks
A review paper on PINNs.
Sunbird
My manuals on working with Sunbird HPC cluster
praksharma's Repositories
praksharma/PINNpapers
Must-read Papers on Physics-Informed Neural Networks.
praksharma/Stiff-PDEs-and-Physics-Informed-Neural-Networks
A review paper on PINNs.
praksharma/Dynamics-Earthquake-Analysis-of-Structures
The main objectives of this individual project are: 1) to further enhance the understanding of the numerical time integration method – Newmark’s algorithm by numerically investigating its accuracy and stability, and by implementing it using a computer programming language (e.g. MatLab, C, C++, or Python); 2) to generate the earthquake response spectra from a particular earthquake; and 3) to undertake an earthquake analysis of a simple frame structure.
praksharma/Sparse-System-Identification
This work presents the application of machine learning models in order to obtain a sparse governing equation of complex fluid dynamics problems.
praksharma/PDE-sparse-identification
Inspired from PDE-FIND
praksharma/ANSYS-automation-using-Python
praksharma/modulus-toolchain
Suite of utilities aiming to simplify the workflow required to build models using Physics Informed Neural Networks and, eventually, Physics ML more broadly. This includes facilities for project management, problem definition, debugging, model configuration and training, and model inference.
praksharma/OpenFoam_Learning
This repository consists of simulation of some benchmark CFD problem.
praksharma/Optimization-codes
EG-M07 module codes
praksharma/Physics-Informed-Neural-Networks
Investigating PINNs
praksharma/PINNs-TF2.0
TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).
praksharma/Blender-renders
This repo contains Blender renders.
praksharma/bosch_torchphysics
PIML
praksharma/env-path-gui
A Tkinter based GUI to modify linux environment paths ($PATH)
praksharma/fourier-neural-operator-simple
praksharma/Geo-FNO
Geometry-Aware Fourier Neural Operator (Geo-FNO)
praksharma/Learning-Python-Physics-Informed-Machine-Learning-PINNs-DeepONets
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
praksharma/NASA_shock
This is the code for "Neural Network Reconstruction of Plasma Space-Time" by C.Bard and J.Dorelli (DOI: 10.3389/fspas.2021.732275). It is a Physics-Informed Transformer Neural Network which was used to reconstruct one-dimensional (M)HD shocktubes from partial samples. Includes source code, data, and jupyter notebooks for scientific reproduction
praksharma/p5.js-Tutorials
praksharma/pbdl-book
Welcome to the Physics-based Deep Learning Book (v0.2)
praksharma/PDE-Ground-truth
A repo with a lot of benchmark problems and analytical solutions to play with.
praksharma/PhyCRNet
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
praksharma/qrcopy
CLI application to share clipboard text to mobile phone using QRcode
praksharma/test
workshop test