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Updated: Jul 5, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

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通过优化离散损失来解决物理中的反向问题:在没有神经网络的情况下快速准确地学习

Petr Karnakov1, Sergey Litvinov1, Petros Koumoutsakos1

  • 1Computational Science and Engineering Laboratory, Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA 02138, USA.

PNAS nexus
|January 22, 2024
PubMed
概括

这项研究引入了优化离散损失 (ODIL) 框架,该框架加速了与神经网络相比,以五个数量级加速解决由部分微分方程 (PDEs) 规范的反向问题. ODIL利用传统的PDE近似和机器学习工具来提高速度和准确性.

关键词:
计算流体动力学的流体动力学.反向问题是反向的问题.部分微分方程部分微分方程.基于物理学的神经网络.

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