针对 PINN 培训的两阶段优化

arXiv - MATH - Optimization and Control Pub Date : 2024-09-11 DOI:arxiv-2409.07296

Dimary Moreno López

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引用次数: 0

摘要

本研究提出了一种训练神经网络的算法，在这种算法中，损失函数可以分解为两个需要最小化的非负项。所提出的方法是对非精确复原算法的改编，构成了一种施加下降条件的两阶段方法。在 PINN 训练中进行了一些性能测试。

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Two-Phase Optimization for PINN Training

This work presents an algorithm for training Neural Networks where the loss function can be decomposed into two non-negative terms to be minimized. The proposed method is an adaptation of Inexact Restoration algorithms, constituting a two-phase method that imposes descent conditions. Some performance tests are carried out in PINN training.

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arXiv - MATH - Optimization and Control

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