A library for scientific machine learning and physics-informed learning
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Updated
Jan 21, 2025 - Python
A library for scientific machine learning and physics-informed learning
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.
Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."
No need to train, he's a smooth operator
Code for training and inferring acoustic wave propagation in 3D
NodeLab is a simple MATLAB-repository for node-generation and adaptive refinement for testing, and implementing various meshfree methods (including physics-informed neural networks, PINNs and DeepOnet) for solving PDEs in arbitrary domains.
Official repo for separable operator networks -- extreme-scale operator learning for parametric PDEs.
Nonlinear model reduction for operator learning
Source code of "On the influence of over-parameterization in manifold based surrogates and deep neural operators".
Benchmarking Surrogates for coupled ODE systems.
We implement a Multifidelity-DeepONet that leverages both high-fidelity CFD simulations and real-time, low-fidelity sensor data. We also proved that Multifidelity-DeepONet has better performance compare to all the others baseline methods in our experiments.
RenONet: Multiscale operator learning for complex social systems
Source code of "Fully Convolutional Network-Enhanced DeepONet-Based Surrogate of Predicting the Travel-Time Fields."
ROMA: Renormalized Operators with Multiscale Attention
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