Rahul Sundar's repositories
CS6910-DeepLearningFundamentals
This repository contains my assignment submissions including datasets, code, documentation and results.
Art-of-Code-A-pythonic-approach
This repository is a part of an online course taken by me for School kids to teach them the fundamental idea behind programming and computer science. This course was primarily taught using Python.
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.
CNN-SINDy-MLROM
Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.
PDE-Identification-Features
Data-driven Identification of 2D Partial Differential Equations using Extracted Physical Features
100-Days-Of-ML-Code
100 Days of ML Coding
AdvancedOptML
CS 7301: Spring 2021 Course on Advanced Topics in Optimization in Machine Learning
awesome-research
A curated list of resources to help with computational research.
Certificates
Contains e-certificates of various conferences/summer schools attended.
deeponet
Learning nonlinear operators via DeepONet
DL-ROM
Deep Learning for Reduced Order Modelling
EQDiscovery
Physics-informed learning of governing equations from scarce data
examples
Example deep learning projects that use wandb's features.
FBPINNs
Reproduces the results of the paper "Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations".
Hybrid_Lorenz-OmerSan
This repository contains codes for paper on nonintrusive hybrid neural-physics modeling of incomplete dynamical systems.
multimodal-dynamics
Code for AAAI 2021 paper "Learning Intuitive Physics with Multimodal Generative Models"
Physics_Informed_NeuralNetwork
Implementation in TF 2.0 of Maziar Raissi's Physics Informed Neural Networks (PINNs) repository.
PIFFGANs
Physics-informed Fourier feature GANs
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 contro
PythonFOAM
In-situ data analyses and machine learning with OpenFOAM and Python
SPINN
Sparse Physics-based and Interpretable Neural Networks
TensorFlow2.0-Examples
🙄 Difficult algorithm, Simple code.
twophasePINN_edits
Physics-informed neural networks for two-phase flow problems
Voronoi-CNN
Sample codes for training of Voronoi-tessellation-assisted convolutional neural network by Fukami et al. (Nature Machine Intelligence 2021)