Rahul Sundar's repositories
ANNS_ODES_PDES
Artificial Neural Networks are universal approximators and in this project, I aim to study the effectiveness of ANNs over traditional numerical methods to solve engineering problems. Specifically statics and dynamics of mechanical structures and non linear odes are looked into for applications. This work is based on the book by Prof Snehashish Chakraverty and Dr. Sumit Kumar Jeswal. I aim to validate their claims in the book "Applied Artificial Neural Network Methods for Engineers and Scientists" and also arrive at possible extensions to the methods discussed in the book.
SciencePlots
Matplotlib styles for scientific plotting
100-Days-Of-ML-Code
100 Days of ML Coding
AdvancedOptML
CS 7301: Spring 2021 Course on Advanced Topics in Optimization in Machine Learning
cnn-infill-optimization
This repository contains parts of the code implemented for my project: Comparison of local vs global geological data for Reservoir oil recovery forecasting
computer-science
:mortar_board: Path to a free self-taught education in Computer Science!
deeponet
Learning nonlinear operators via DeepONet
DL-ROM-Meth
Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on arXiv: arXiv:2001.04001
dmd_autoencoder
Leveraging deep learning to find an approximation of the Koopman operator.
examples
Example deep learning projects that use wandb's features.
fourier_neural_operator
Use Fourier transform to learn operators in differential equations.
From-Physics-To-GANs
Code for the blog post
gpubootcamp
This repository consists for gpu bootcamp material for HPC and AI
multimodal-dynamics
Code for AAAI 2021 paper "Learning Intuitive Physics with Multimodal Generative Models"
PIML
My pytorch based implementation of the paper 'Limitations of Physics Informed Machine Learning for Nonlinear Two-Phase Transport in Porous Media'
PINN-laminar-flow
Physics-informed neural network for solving fluid dynamics problems
POD-DL-ROM
Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition. Available on arXiv: arXiv:2101.11845
PythonFOAM
In-situ data analyses and machine learning with OpenFOAM and Python
PyTorch-Tutorial
Build your neural network easy and fast, 莫烦Python中文教学
Symbolic-Pursuit
Github for the NIPS 2020 paper "Learning outside the black-box: at the pursuit of interpretable models"
Tensorflow-Tutorial
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
TensorFlow2.0-Examples
🙄 Difficult algorithm, Simple code.
the-incredible-pytorch
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
twophasePINN_edits
Physics-informed neural networks for two-phase flow problems
Yolov5_tf
Yolov5/Yolov4/ Yolov3/ Yolo_tiny in tensorflow