Rahul Sundar (RahulSundar)

RahulSundar

Geek Repo

Company:Bio-mimetics Lab, Indian Institute of Technology Madras

Location:Chennai

Home Page:https://in.linkedin.com/in/rahul-sundar-311a6977

Twitter:@RahulSundar6

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Organizations
biomimetics-iitm

Rahul Sundar's repositories

Applied-Deep-Learning

Applied Deep Learning Course

characterizing-pinns-failure-modes

Characterizing possible failure modes in physics-informed neural networks.

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aa_autoencoder_mca

Supporting code for "reduced order modeling using advection-aware autoencoders"

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awesome-network-analysis

A curated list of awesome network analysis resources.

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best-of-streamlit

🏆 A ranked gallery of awesome streamlit apps built by the community

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Conservative_PINNs

We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation property of cPINN is obtained by enforcing the flux continuity in the strong form along the sub-domain interfaces.

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deeponet-fno

DeepONet & FNO (with practical extensions)

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generative-models

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

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gpinn

gPINN: Gradient-enhanced physics-informed neural networks

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hpinn

hPINN: Physics-informed neural networks with hard constraints

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ModalPINN_Python_code

Publication of Python code used to train ModalPINN

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multifidelity-deeponet

Multifidelity DeepONet

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Natural-climate-reconstruction-with-VAE

Final project for the course Laboratory of Computational Physics mod. B, master degree in Physics of Data

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normcap

OCR powered screen-capture tool to capture information instead of images

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PB-GAN

Physics-based regularization using generative adversarial networks

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piDMD

MATLAB codes for physics-informed dynamic mode decomposition (piDMD)

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PINN-for-Poisson-Equation

This repo contains the code for solving Poisson Equation using Physics Informed Neural Networks

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PINNpapers_revised

Must-read Papers on Physics-Informed Neural Networks.

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pytorch-vae-simple

A Variational Autoencoder (VAE) implemented in PyTorch

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rang_pinn

Code for reproducing the paper: RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks

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Rowdy_Activation_Functions

We propose Deep Kronecker Neural Network, which is a general framework for neural networks with adaptive activation functions. In particular we proposed Rowdy activation functions that inject sinusoidal fluctuations thereby allows the optimizer to exploit more and train the network faster. Various test cases ranging from function approximation, inferring the PDE solution, and the standard deep learning benchmarks like MNIST, CIFAR-10, CIFAR-100, SVHN etc are solved to show the efficacy of the proposed activation functions.

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sbinn

SBINN: Systems-biology informed neural network

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tfc

The Theory of Functional Connections: A functional interpolation method with applications in solving differential equations.

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vae_mhd_solver

Variational Autoencoder (VAE)-like neural network to solve ideal MHD equilibrium in a tokamak

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XPINNs

Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

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XPINNs_TensorFlow-2

XPINN code written in TensorFlow 2 (2x to 3x times faster than TensorFlow 1 code)

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