A library for scientific machine learning and physics-informed learning
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Updated
Jul 17, 2024 - Python
A library for scientific machine learning and physics-informed learning
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
Physics-Informed Neural networks for Advanced modeling
Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.
Physics-informed neural network for solving fluid dynamics problems
Neural network based solvers for partial differential equations and inverse problems 🌌. Implementation of physics-informed neural networks in pytorch.
A large-scale benchmark for machine learning methods in fluid dynamics
Deep learning library for solving differential equations on top of PyTorch.
Generative Pre-Trained Physics-Informed Neural Networks Implementation
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 cond…
Here I will try to implement the solution of PDEs using PINN on pytorch for educational purpose
DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations
A remix of Arch Linux ARM for Raspberry Pi 3 B+ built for HackRF and RTL-SDR
PiNN2 is a easy-to-use framework for device compact modeling using physics-inspired neural networks
The Poisson equation is an integral part of many physical phenomena, yet its computation is often time-consuming. This module presents an efficient method using physics-informed neural networks (PINNs) to rapidly solve arbitrary 2D Poisson problems.
FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries
Machine Learning-based Second-order Analysis of Beam-columns through Physics-Informed Neural Networks
This project is divided in a two parts. In first study, Lame parameters are identified using tanh activation function. After that, six activation functions are analysed on the basis of minimum loss, training time and convergence order for different error norms.
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