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New research details kernel-based operator learning with error analysis and physics-informed extensions

By PulseAugur Editorial · [4 sources] ·

Researchers have published new work on kernel-based operator learning, detailing error analysis and budget allocation strategies. The study introduces a two-stage framework involving offline regression and online reconstruction operators, establishing a condition for balancing training data, input observations, and output resolution. Additionally, a physics-informed extension is proposed that incorporates knowledge of partial differential equations without retraining, demonstrating effectiveness through numerical experiments. AI

IMPACT Advances theoretical understanding in operator learning, potentially improving the efficiency and accuracy of AI models in scientific applications.

RANK_REASON The cluster contains two arXiv preprints discussing theoretical aspects of operator learning, including error analysis and convergence rates.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New research details kernel-based operator learning with error analysis and physics-informed extensions

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The cluster contains two arXiv preprints discussing theoretical aspects of operator learning, including error analysis and convergence rates.
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94 days old
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COVERAGE [4]

  1. arXiv cs.LG TIER_1 English(EN) · R\"udiger Kempf ·

    Kernel-based Operator Learning: Error Analysis, Budget Allocation, and a Physics-Informed Extension

    arXiv:2607.06287v1 Announce Type: cross Abstract: We study kernel-based operator learning in a two-stage sampling framework, where an offline kernel regression operator learns a discretized representation of the target operator from input-output pairs and an online kernel reconst…

  2. arXiv cs.LG TIER_1 English(EN) · Rüdiger Kempf ·

    Kernel-based Operator Learning: Error Analysis, Budget Allocation, and a Physics-Informed Extension

    We study kernel-based operator learning in a two-stage sampling framework, where an offline kernel regression operator learns a discretized representation of the target operator from input-output pairs and an online kernel reconstruction operator recovers the output function from…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Kernel-based Operator Learning: Error Analysis, Budget Allocation, and a Physics-Informed Extension

    We study kernel-based operator learning in a two-stage sampling framework, where an offline kernel regression operator learns a discretized representation of the target operator from input-output pairs and an online kernel reconstruction operator recovers the output function from…

  4. arXiv cs.LG TIER_1 English(EN) · Simone Brugiapaglia, Nicola Rares Franco, Nicholas H. Nelsen ·

    A short tour of operator learning theory: Convergence rates, statistical limits, and open questions

    arXiv:2603.00819v2 Announce Type: replace-cross Abstract: This paper surveys recent developments at the intersection of operator learning, statistical learning theory, and approximation theory. First, it reviews error bounds for empirical risk minimization with a focus on holomor…

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