Scientific Learning project on the monodomain equation
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Updated
Jun 12, 2024 - Python
Scientific Learning project on the monodomain equation
EIT-EBM
Physics-Informed Neural networks for Advanced modeling
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
A library for scientific machine learning and physics-informed learning
Generative Pre-Trained Physics-Informed Neural Networks Implementation
The application of a Physics Informed Neural Network on modelling the parameters of a Continuously Stirred Tank Reactor, based on the data generated by a Simulink model.
FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries
Fast lightweight physical informed multi-exposure fusion model.
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
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
A repo to learn and curate PINN
This repository contains the machine learning projects completed for the class "Deep Learning in Scientific Computing" taught at ETH jointly by Siddhartha Mishra and Benjamin Moseley in Spring 2024. The description of the tasks can be found in the PDFs.
A large-scale benchmark for machine learning methods in fluid dynamics
Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.
Notebooks and Data for ICA's course on IA
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