English(EN) Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial $\mathrm{SrTiO_3}$ on Si memristors via Dynamic Spectral Optimization

新型PINN架构模拟钛酸锶忆阻器动力学

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

研究人员开发了一种新颖的物理信息神经网络（PINN）架构，用于模拟钛酸锶忆阻异质结构中的复杂离子-电子传输。这种级联PINN方法结合了专用的谱优化器，能有效处理传统方法面临的数值刚度和多尺度空间差异。训练好的代理模型能够准确重现实验中的电流-电压滞后现象，并确保严格的泊松一致性，为逆参数估计和推理提供了比传统有限元求解器更高效、可微分的替代方案。 AI

影响引入了一种更高效、可微分的方法来模拟复杂的材料传输，有望加速材料发现和器件优化。

排序理由详细介绍材料科学新建模技术的学术论文。[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) · Rodion Podorozhny, Nikoleta Theodoropoulou, Jelena Te\v{s}i\'c · 2026-09-04 04:00

用于外延 SrTiO3/Si 忆阻器中氧空位动力学的物理信息神经网络代理，通过动态光谱优化实现

arXiv:2609.02966v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) offer a promising framework for modeling semiconductor devices, yet standard architectures struggle with severe numerical stiffness and multiscale spatial discrepancies inherent to oxide he…

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报道来源 [1]

用于外延 SrTiO3/Si 忆阻器中氧空位动力学的物理信息神经网络代理，通过动态光谱优化实现

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