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New method enables on-the-fly derivative-informed training for neural operators

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called sketched tangent consistency loss (sTCL) for training neural operators. This technique allows for on-the-fly derivative-informed training, eliminating the need for slow and storage-intensive offline derivative labels. sTCL enforces sensitivity consistency directly from the governing equation using randomly sketched input perturbations, making it adaptable across different datasets and resolutions. The method has demonstrated comparable accuracy to existing offline methods across various equations, including Navier-Stokes, while significantly streamlining the training process. AI

IMPACT Streamlines the training of neural operators, potentially accelerating their use in inverse problems and control systems.

RANK_REASON Academic paper detailing a new method for training neural operators. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method enables on-the-fly derivative-informed training for neural operators

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Academic paper detailing a new method for training neural operators. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xinhan Yang, Lu Lu, Shancong Mou ·

    Derivative-Informed Training of Neural Operators On-the-Fly via Sketched Tangent Consistency

    arXiv:2609.32797v2 Announce Type: replace Abstract: Derivative-informed training improves neural operators by directly supervising their input-output sensitivities, which is crucial when neural operators are used as differentiable surrogates for inverse problems, PDE-constrained …

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