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Spectral Audit of In-Context Operator Networks

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Existing evaluations of neural operators and in-context operator learning rely primarily on prediction error, but accurate output prediction does not guarantee the correct local dynamical structure. A model may match solutions while exhibiting incorrect sensitivities, distorted frequency response, spurious mode coupling, or unstable tangent behavior.

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arXiv Machine Learning

Jun 2

Spectral Audit of In-Context Operator Networks

arXiv:2606. 02427v1 Announce Type: cross Abstract: Existing evaluations of neural operators and in-context operator learning rely primarily on prediction error, but accurate output prediction does not guarantee the correct local dynamical structure.

By Zhiwei Gao, Liu Yang, George Em Karniadakis

diffusion benchmarks

arXiv AI

Jun 11

SirenFNO: Efficient and Full Frequency Learning of Fourier Neural Operators

arXiv:2606. 11518v1 Announce Type: cross Abstract: Fourier neural operators (FNOs) are effective and efficient surrogates for approximating solutions of PDEs and generalize across discretizations.

By Pengqing Shi, Jie Yin, Stephen Tierney, Junbin Gao

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arXiv AI

Jun 29

Higher-Order Fourier Neural Operator: Explicit Mode Mixer for Nonlinear PDEs

arXiv:2606. 28122v1 Announce Type: cross Abstract: Neural operators provide deep neural networks for learning mappings between function spaces.

By Alex Colagrande, Paul Caillon, Eva Feillet, Alexandre Allauzen

llms benchmarks safety

arXiv AI

6d ago

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By Jiaquan Zhang, Shuxu Chen, Haifan Meng, Yi Lu, Zhihan Lyu, Fan Mo, Wei Dong, Yang Yang, Chaoning Zhang

benchmarks

arXiv Machine Learning

Jun 16

Manifold-Orthogonal Dual-spectrum Extrapolation for Parameterized Physics-Informed Neural Networks

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By Zhangyong Liang, Huanhuan Gao

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arXiv Machine Learning

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Spectral Embedding via Chebyshev Bases for Robust DeepONet Approximation

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By Muhammad Abid, Omer San

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Spectral Audit of In-Context Operator Networks

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