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Physics-based active learning boosts neural operator training efficiency

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a new active learning technique called physics-based acquisition to improve the efficiency of training neural operators for solving partial differential equations. This method uses the equation's residual to intelligently select the most informative data samples, reducing the overall data requirements for training. Experiments on the 1D Burgers equation and 2D compressible Navier-Stokes equations demonstrate that this approach outperforms random acquisition and matches state-of-the-art data efficiency while incorporating a physics-based inductive bias. AI

IMPACT Enhances data efficiency in training neural operators for scientific simulations, potentially accelerating discovery in fields relying on solving differential equations.

RANK_REASON The cluster contains an academic paper detailing a new method for training neural operators.

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AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Han Wan, Rui Zhang, Hao Sun ·

    Spectral-inspired Operator Learning with Limited Data and Unknown Physics

    arXiv:2505.21573v3 Announce Type: replace-cross Abstract: Learning PDE dynamics from limited data with unknown physics is challenging. Existing neural PDE solvers either require large datasets or rely on known physics (e.g., PDE residuals or handcrafted stencils), leading to limi…

  2. arXiv cs.AI TIER_1 English(EN) · Alicja Polanska, Lorenzo Zanisi, Vignesh Gopakumar, Stanislas Pamela ·

    Data-Efficient Neural Operator Training via Physics-Based Active Learning

    arXiv:2605.21348v1 Announce Type: cross Abstract: Solving partial differential equations with neural operators significantly reduces computational costs but remains bottlenecked by high training data requirements. Active learning offers a natural framework to mitigate this by sel…

  3. arXiv cs.AI TIER_1 English(EN) · Stanislas Pamela ·

    Data-Efficient Neural Operator Training via Physics-Based Active Learning

    Solving partial differential equations with neural operators significantly reduces computational costs but remains bottlenecked by high training data requirements. Active learning offers a natural framework to mitigate this by selectively acquiring the most informative samples in…

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