English(EN) A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems

物理信息神经网络模拟双材料系统中的波传播

作者 PulseAugur 编辑部 · [3 个来源] · 2026-07-07 16:34

研究人员开发了一个利用物理信息神经网络（PINNs）模拟双材料系统中弹性动力学波传播的新颖框架。该方法将物理定律直接嵌入神经网络，能够准确预测材料界面上的波传输和反射。该框架已通过高保真有限元模拟进行了验证，并证明了其作为连续代理模型的能力，无需额外的计算成本即可预测未见过条件下的响应。 AI

影响该框架为工程领域复杂的物理模拟提供了一种更有效、更准确的代理建模方法。

排序理由该集群包含一篇学术论文，详细介绍了将PINNs应用于特定科学问题的新的研究框架。

AI 生成摘要 · Google Gemini · 来自 3 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Sonal Ankush Chibire, Jenn-Terng Gau, Bo Zhang · 2026-07-08 04:00

用于双材料系统中弹性动力波传播的物理信息神经网络框架

arXiv:2607.06479v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a promising framework for solving partial differential equations while embedding the underlying physical laws directly into the learning process. This study presents a PINN-based fram…
arXiv cs.AI TIER_1 English(EN) · Bo Zhang · 2026-07-07 16:34

用于双材料系统中弹性动力波传播的物理信息神经网络框架

Physics-informed neural networks (PINNs) provide a promising framework for solving partial differential equations while embedding the underlying physical laws directly into the learning process. This study presents a PINN-based framework for modeling transient elastodynamic wave …
Hugging Face Daily Papers TIER_1 English(EN) · 2026-07-07 16:34

用于双材料系统中弹性动力波传播的物理信息神经网络框架

Physics-informed neural networks (PINNs) provide a promising framework for solving partial differential equations while embedding the underlying physical laws directly into the learning process. This study presents a PINN-based framework for modeling transient elastodynamic wave …