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Found 9 papers, 6 papers with code
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
Pre-training has been investigated to improve the efficiency and performance of training neural operators in data-scarce settings.
Preconditioning for Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs).
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs
In addition to providing a standardized means of assessing performance, PINNacle also offers an in-depth analysis to guide future research, particularly in areas such as domain decomposition methods and loss reweighting for handling multi-scale problems and complex geometry.
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks
Physics-informed Neural Networks (PINNs) have recently achieved remarkable progress in solving Partial Differential Equations (PDEs) in various fields by minimizing a weighted sum of PDE loss and boundary loss.
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data
The neural operator has emerged as a powerful tool in learning mappings between function spaces in PDEs.
Task Aware Dreamer for Task Generalization in Reinforcement Learning
Extensive experiments in both image-based and state-based tasks show that TAD can significantly improve the performance of handling different tasks simultaneously, especially for those with high TDR, and display a strong generalization ability to unseen tasks.
GNOT: A General Neural Operator Transformer for Operator Learning
However, there are several challenges for learning operators in practical applications like the irregular mesh, multiple input functions, and complexity of the PDEs' solution.
Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
Recent work shows that it provides potential benefits for machine learning models by incorporating the physical prior and collected data, which makes the intersection of machine learning and physics become a prevailing paradigm.
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs
We present a unified hard-constraint framework for solving geometrically complex PDEs with neural networks, where the most commonly used Dirichlet, Neumann, and Robin boundary conditions (BCs) are considered.
