Topic: physics-informed-neural-networks Goto Github
Some thing interesting about physics-informed-neural-networks
Some thing interesting about physics-informed-neural-networks
physics-informed-neural-networks,A Physics-Informed Neural Network for solving Burgers' equation.
User: 314arhaam
physics-informed-neural-networks,A Physics-Informed Neural Network to solve 2D steady-state heat equations.
User: 314arhaam
physics-informed-neural-networks,This project contains a collection of deep learning models developed by the AI4Sim team with various partners. This is structured on the basis of use-cases providing canonical PyTorch Lightning pipelines allowing to train neural network models that are able to surrogate various physical processes.
Organization: ai4sim
physics-informed-neural-networks,Simplified implementation of locally adaptive activation functions (LAAF) with slope recovery for deep and physics-informed neural networks (PINNs) in PyTorch.
User: akapet00
physics-informed-neural-networks,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
User: alexpapados
physics-informed-neural-networks,Example problems in Physics informed neural network in JAX
User: asem000
physics-informed-neural-networks,Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)
User: benmoseley
physics-informed-neural-networks,Code accompanying my blog post: So, what is a physics-informed neural network?
User: benmoseley
physics-informed-neural-networks,FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries
Organization: cmgcds
Home Page: https://cmgcds.github.io/fastvpinns/
physics-informed-neural-networks,Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper "Physics-informed neural networks for 1D sound field predictions with parameterized sources and impedance boundaries" by N. Borrel-Jensen, A. P. Engsig-Karup, and C. Jeong.
Organization: dtu-act
physics-informed-neural-networks,Here I will try to implement the solution of PDEs using PINN on pytorch for educational purpose
User: erfanhamdi
physics-informed-neural-networks,A toolkit with data-driven pipelines for physics-informed machine learning.
Organization: ibm
Home Page: https://ibm.github.io/simulai/
physics-informed-neural-networks,IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.
Organization: idrl-lab
physics-informed-neural-networks,Implement PINN with high level APIs of TF2.0, including a solution of coupled PDEs with PINN
User: ippqw5
physics-informed-neural-networks,Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
User: jbramburger
physics-informed-neural-networks,Awesome-spatial-temporal-data-mining-packages. Julia and Python resources on spatial and temporal data mining. Mathematical epidemiology as an application. Most about package information. Data Sources Links and Epidemic Repos are also included. Keep updating.
Organization: juliaepi
Home Page: https://juliaepi.github.io/MathEpiDeepLearning/
physics-informed-neural-networks,An interface for accelerated simulation of high-dimensional collisionless and electrostatic plasmas.
User: killah-t-cell
physics-informed-neural-networks,python library for atomistic machine learning
Organization: lanl
Home Page: https://lanl.github.io/hippynn/
physics-informed-neural-networks,EP-PINNs implementation for 1D and 2D forward and inverse solvers for the Aliev-Panfilov cardiac electrophysiology model. Also includes Matlab finite-differences solver for data generation.
User: martavarela
physics-informed-neural-networks,Repository for Gravity Field Modeling and Recovery using Machine Learning Methods
User: martinastro
physics-informed-neural-networks,A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software
User: martinuzzifrancesco
physics-informed-neural-networks,Physics-Informed Neural networks for Advanced modeling
Organization: mathlab
Home Page: https://mathlab.github.io/PINA/
physics-informed-neural-networks,DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations
User: mjfadeaway
Home Page: https://arxiv.org/abs/2112.14038
physics-informed-neural-networks,Simple PyTorch Implementation of Physics Informed Neural Network (PINN)
User: nanditadoloi
physics-informed-neural-networks,Using NVIDIA modulus for airfoil optimizations at different angles.
User: neo-fetch
physics-informed-neural-networks,A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
Organization: neurodiffgym
Home Page: http://pypi.org/project/neurodiffeq/
physics-informed-neural-networks,Yet another PINN implementation
User: nimahsn
physics-informed-neural-networks,OpenFOAM and Machine Learning Hackathon
Organization: ofdatacommittee
physics-informed-neural-networks,This repository containts materials for End-to-End AI for Science
Organization: openhackathons-org
physics-informed-neural-networks,Deep learning framework for model reduction of dynamical systems
User: panchgonzalez
Home Page: https://arxiv.org/abs/1808.01346
physics-informed-neural-networks,PDEBench: An Extensive Benchmark for Scientific Machine Learning
Organization: pdebench
physics-informed-neural-networks,resources pour le cours d'introduction à la programmation des GPUs du mastère spécialisé HPC-AI
User: pkestene
physics-informed-neural-networks,PINNs-TF2, Physics-informed Neural Networks (PINNs) implemented in TensorFlow V2.
User: rezaakb
physics-informed-neural-networks,PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.
User: rezaakb
physics-informed-neural-networks,To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to the direction of travel of information in convection-diffusion equations, i.e., method of characteristic; The repository includes a pytorch implementation of PINN and proposed LPINN with periodic boundary conditions
User: rmojgani
physics-informed-neural-networks,Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Organization: sciml
Home Page: https://docs.sciml.ai/SciMLTutorialsOutput/stable/
physics-informed-neural-networks,Neural Eikonal Solver: framework for modeling traveltimes via solving eikonal equation using neural networks
User: sgrubas
Home Page: https://sgrubas.github.io/NES/
physics-informed-neural-networks,Gradient-based adaptive sampling algorithms for self-supervising PINNs
User: shashanksubramanian
physics-informed-neural-networks,A pytorch implementaion of physics informed neural networks for two dimensional NS equation
User: shengfeng233
physics-informed-neural-networks,Generative Pre-Trained Physics-Informed Neural Networks Implementation
User: skoohy
physics-informed-neural-networks,Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Organization: tensordiffeq
Home Page: http://docs.tensordiffeq.io
physics-informed-neural-networks,Code for our RSS'21 paper: "Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control"
User: thaipduong
Home Page: https://thaipduong.github.io/SE3HamDL/
physics-informed-neural-networks,A JAX-based research framework for differentiable and parallelizable acoustic simulations, on CPU, GPUs and TPUs
Organization: ucl-bug
physics-informed-neural-networks,A machine learning boosted parallel-in-time differential equation solver framework.
User: viktorc
physics-informed-neural-networks,NVFi in PyTorch (NeurIPS 2023)
Organization: vlar-group
physics-informed-neural-networks,Physics-informed neural networks for highly compressible flows 🧠🌊
User: wagenaartje
Home Page: https://repository.tudelft.nl/islandora/object/uuid:6fd86786-153e-4c98-b4e2-8fa36f90eb2a
physics-informed-neural-networks,Optimizing Physics-Informed NN using Multi-task Likelihood Loss Balance Algorithm and Adaptive Activation Function Algorithm
User: xinyuanliao
physics-informed-neural-networks,A Framework for Remaining Useful Life Prediction Based on Self-Attention and Physics-Informed Neural Networks
User: xinyuanliao
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