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7 search results found

Neuralpde.jl ⭐ 864

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Diffeqflux.jl ⭐ 810

Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

Pdebench ⭐ 522

PDEBench: An Extensive Benchmark for Scientific Machine Learning

18s096sciml ⭐ 268

18.S096 - Applications of Scientific Machine Learning

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

Neuralsolvers ⭐ 103

Neural network based solvers for partial differential equations and inverse problems 🌌. Implementation of physics-informed neural networks in pytorch.

Pinns Tf2.0 ⭐ 91

TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).

Mljc Unito Projectx 2020 Public ⭐ 29

Public repository for the proposal “Physics-Informed Machine Learning Simulator for Wildfire Propagation” - MLJC University of Turin - ProjectX2020 Competition (UofT AI)

Gpt Pinn ⭐ 22

Generative Pre-Trained Physics-Informed Neural Networks Implementation

Deep_kolmogorov ⭐ 18

Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning (NeurIPS 2020)

Schrodingers Equation Solution By Neural Network ⭐ 17

Artifitial Neural Networks for Solving Ordinary and Partial Differential Equations, in this case, Schrodinger's Equation for One Particle in a 1-Dimentional Box

Pseudo Hamiltonian Neural Networks ⭐ 10

The package phlearn for modelling pseudo-Hamiltonian systems by pseudo-Hamiltonian neural networks (PHNN), for ODEs and PDEs

Uqpinns Tf2.0 ⭐ 5

TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks (UQPINNs).

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