arXiv:2412.06686
공식 코드 · Papers with Code
Some Best Practices in Operator Learning
원문 초록
Hyperparameters searches are computationally expensive. This paper studies some general choices of hyperparameters and training methods specifically for operator learning. It considers the architectures DeepONets, Fourier neural operators and Koopman autoencoders for several differential equations to find robust trends. Some options considered are activation functions, dropout and stochastic weight averaging.
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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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