New framework improves neural operators' handling of discontinuities

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-19 13:17

Researchers have developed a new framework called Cut-DeepONet to improve how neural operators handle discontinuities and sharp transitions in partial differential equations. This method partitions the domain into smooth regions and represents discontinuities in a higher-dimensional space, avoiding direct approximation. Experiments show Cut-DeepONet outperforms existing methods, even with low-resolution data, by using fewer parameters and changing the problem's representation. AI

影响 Enhances the ability of neural networks to model complex physical phenomena with sharp transitions.

排序理由 The cluster contains an academic paper detailing a new method for neural operators.

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报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Ha Dang, Sebastian Schmidt, Juergen Hesser · 2026-05-20 04:00

Smooth Piecewise Cutting for Neural Operator to Handle Discontinuities and Sharp Transitions

arXiv:2605.19823v1 Announce Type: cross Abstract: Neural operators have achieved strong performance in learning solution operators of partial differential equations (PDEs), but their inherently continuous representations struggle to capture discontinuities and sharp transitions. …
arXiv stat.ML TIER_1 English(EN) · Juergen Hesser · 2026-05-19 13:17

Smooth Piecewise Cutting for Neural Operator to Handle Discontinuities and Sharp Transitions

Neural operators have achieved strong performance in learning solution operators of partial differential equations (PDEs), but their inherently continuous representations struggle to capture discontinuities and sharp transitions. Existing approaches typically approximate such fea…

报道来源 [2]

Smooth Piecewise Cutting for Neural Operator to Handle Discontinuities and Sharp Transitions

Smooth Piecewise Cutting for Neural Operator to Handle Discontinuities and Sharp Transitions

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