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New SS-ESOAP method enhances physics-informed neural network training

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SS-ESOAP, a novel preconditioning method designed to improve the training of physics-informed neural networks (PINNs). This method addresses the challenge of ill-conditioned objectives that often hinder high-accuracy training in PINNs. SS-ESOAP combines adaptive basis updates with a scalar secant-energy correction and variance-state downscaling, outperforming existing methods like SOAP and Adam on several physics-based benchmarks, including Burgers and Boussinesq equations. AI

IMPACT This method offers a scalable approach for achieving high accuracy in stiff, physics-informed training scenarios.

RANK_REASON The cluster contains a research paper detailing a new method for physics-informed learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New SS-ESOAP method enhances physics-informed neural network training

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The cluster contains a research paper detailing a new method for physics-informed learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Guangyuan Wang, Mads Toftrup, Sebastian Loeschcke, Yixuan Wang, Anima Anandkumar ·

    SS-ESOAP: Self-Scaled Adaptive Preconditioning for Physics-Informed Learning

    arXiv:2608.29448v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) often face ill-conditioned objectives that limit high-accuracy training. Dense quasi-Newton methods improve local conditioning but require expensive optimizer state, while Kronecker-factore…

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