arXiv:2412.10354
공식 코드 · Papers with Code
A Library for Learning Neural Operators
원문 초록
We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimensional Euclidean spaces. They can be trained and inferenced on input and output functions given at various discretizations, satisfying a discretization convergence properties. Built on top of PyTorch, NeuralOperator provides all the tools for training and deploying neural operator models, as well as developing new ones, in a high-quality, tested, open-source package. It combines cutting-edge models and customizability with a gentle learning curve and simple user interface for newcomers.
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Mesh-Informed Neural Operator : A Transformer Generative Approach
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
Learning Where to Learn: Training Distribution Selection for Provable OOD Performance
Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains
Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time Generalization
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