arXiv Machine Learning June 30, 2026 By Muhammad Abid, Omer San

Spectral Embedding via Chebyshev Bases for Robust DeepONet Approximation

Read the original on arXiv Machine Learning →

arXiv:2512. 09165v2 Announce Type: replace Abstract: Deep Operator Networks (DeepONets) have emerged as a powerful framework for data-driven operator learning, providing flexible surrogates for nonlinear mappings arising in partial differential equations (PDEs).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 16

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

arXiv:2603. 13751v2 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) have achieved notable success in modeling dynamical systems governed by partial differential equations (PDEs).

By Zhangyong Liang, Huanhuan Gao

diffusion fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 5

Reformulating Neural Operators in $d+1$ Dimensions for Embedding Evolution

arXiv:2505. 11766v4 Announce Type: replace Abstract: Neural Operators (NOs) are powerful architectures for learning mappings between function spaces.

By Haoze Song, Zhihao Li, Xiaobo Zhang, Zecheng Gan, Zhilu Lai, Wei Wang

rag benchmarks

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 Machine Learning

Jun 17

Operator Boosting Produces Pareto-Efficient PDE Surrogates

arXiv:2606. 17460v1 Announce Type: new Abstract: Neural operators are widely used as surrogate solution maps for partial differential equations (PDEs), but full-size models can be costly to store, deploy, and evaluate in many-query scientific workflows.

By Lennon J. Shikhman

benchmarks

arXiv Machine Learning

5d ago

Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations

arXiv:2602. 12706v2 Announce Type: replace Abstract: Neural operators have emerged as fast surrogate solvers for parametric partial differential equations (PDEs).

By Heechang Kim, Qianying Cao, Hyomin Shin, Seungchul Lee, George Em Karniadakis, Minseok Choi

rag diffusion benchmarks

arXiv Machine Learning

Jun 4

Deep Embedded Multiplicative DMD for Algebra-Preserving Koopman Learning

arXiv:2606. 05131v1 Announce Type: new Abstract: Koopman theory turns nonlinear dynamics into a linear spectral problem.

By Kelan Gray, Finlay Brown, Nicolas Boull\'e, Matthew J. Colbrook

diffusion

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