English(EN) Adaptive Domain Decomposition for Finite Basis Physics-Informed Neural Networks

新AI方法增强了物理信息神经网络的域分解

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员推出了一种名为自适应域分解有限基物理信息神经网络（ADD-FBPINNs）的新方法，该方法将单位分割网络（POUNets）与有限基物理信息神经网络（FBPINNs）相结合。该方法通过使用POUNets学习数据驱动的分割来增强域分解，从而实现更稳定和可解释的几何分割。与采用固定域划分的传统FBPINNs相比，ADD-FBPINNs框架在亥姆霍兹方程、泊松方程和粘性Burgers方程等各种方程上都显示出更高的准确性。 AI

影响通过改进神经网络处理域分解的方式，该方法有望提高复杂物理系统的模拟效率和准确性。

排序理由该集群包含一篇详细介绍物理信息神经网络新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Sarah Helfert, Wanxin Chen, Amanda A. Howard, Panos Stinis, Alexander Heinlein · 2026-11-05 05:00

自适应域分解用于基于有限基的物理信息神经网络

arXiv:2611.05659v1 Announce Type: new Abstract: In this work, we present adaptive domain decomposition finite basis physics-informed neural networks (ADD-FBPINNs). The proposed method integrates Partition of Unity Networks (POUNets), a data-driven approach for learning domain par…

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自适应域分解用于基于有限基的物理信息神经网络

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