#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
A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software
#计算机科学#Deep learning library for solving differential equations on top of PyTorch.
Here I will try to implement the solution of PDEs using PINN on pytorch for educational purpose
Using PINN based MPC for motion planning for SDC and LSTM for pedestrain's trajectory prediction as dynamic obstacles
#计算机科学#FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries
#计算机科学#DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations