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基于物理学的神经网络用于构成模型和粘弹性材料中的多物理合:应用到青路面力学

Xue Luo¹, Jiale Huang¹, Li'an Shen¹

¹Zhejiang University, Hangzhou, Zhejiang 310058, China.

Neural networks : the official journal of the International Neural Network Society

|March 11, 2026

概括

基于物理学的神经网络 (PINNs) 为粘弹性材料提供了先进的建模,克服了传统方法的局限性. 这次审查强调了PINNs.

科学领域:

材料科学材料科学材料科学
计算力学计算力学计算力学
人工智能的人工智能

背景情况:

粘弹性材料表现出复杂的非线性,时间依赖和多物理行为,挑战传统的建模方法.
传统的构成模型往往需要大量的实验数据和物理假设,限制多尺度现象的准确性.
现有的方法在构成反转,高维输入和粘性弹性参数不确定性方面扎.

研究的目的:

提供对最近应用物理信息神经网络 (PINNs) 对粘性弹性材料和结构的进展进行全面的审查.
分析PINNs在建模和解决粘弹性行为的传统方法上的优势.
评估PINNs在模拟各种外部领域和复杂条件下的响应方面的表现.

主要方法:

对PINNs的物理数据融合机制进行系统分析,以提高准确性和适用性.
在温度,湿度和氧化衰老下模拟粘弹性反应时评估PINNs.
简要介绍了诸如注意力机制,自适应抽样和转移学习等尖端技术,以增强PINN模型.

主要成果:

与传统方法相比,PINNs在粘弹性材料建模和解决方案的准确性和适用性方面取得了显著的改进.
在模拟粘弹性反应中,PINN显示了适应高维合,边界不连续性和反向配方的适应性.
先进的技术提高了基于PINN的复杂材料行为模型的准确性和效率.

结论:

PINNs为模拟粘弹性材料提供了一个强大的替代方案,克服了传统数值方法的许多局限性.
尽管在趋同稳定性和多域合方面存在挑战,PINNs为先进的材料分析提供了有前途的途径.
未来的研究将PINNs与多忠度建模和模拟实验融合相结合,将为粘性弹性系统推进智能预测工具.

关键词:

构成模型的建构模型.构成性的解决方法多物理合器基于物理学的神经网络 (PINNs)粘性弹性材料是一种粘性弹性材料.

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