#计算机科学#A library for scientific machine learning and physics-informed learning
#计算机科学#Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
#计算机科学#A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
#计算机科学#Physics-Informed Neural networks for Advanced modeling
Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.
#计算机科学#Physics-informed neural network for solving fluid dynamics problems
#计算机科学# A large-scale benchmark for machine learning methods in fluid dynamics
#计算机科学#This repository containts materials for End-to-End AI for Science
Neural network based solvers for partial differential equations and inverse problems 🌌. Implementation of physics-informed neural networks in pytorch.
PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations
#计算机科学#Generative Pre-Trained Physics-Informed Neural Networks Implementation
Example problems in Physics informed neural network in JAX
#计算机科学#Deep learning library for solving differential equations on top of PyTorch.
A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software
Here I will try to implement the solution of PDEs using PINN on pytorch for educational purpose
#计算机科学#FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries
Using PINN based MPC for motion planning for SDC and LSTM for pedestrain's trajectory prediction as dynamic obstacles
#计算机科学#DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations