标题： 考虑温度的循环神经算子用于HCP材料温度依赖各向异性塑性的研究 显示英文标题

标题： Temperature-Aware Recurrent Neural Operator for Temperature-Dependent Anisotropic Plasticity in HCP Materials

摘要： 神经网络代理模型在计算力学中的本构定律中已经使用了一段时间。 在塑性力学中，这些模型通常依赖于门控循环单元（GRUs）或长短期记忆（LSTM）单元，它们擅长捕捉路径依赖现象。 然而，它们存在训练时间长和与时间分辨率相关的预测结果，这些预测效果较差。 此外，大多数现有的宏观或介观塑性代理模型仅能处理相对简单的材料行为。 为了克服这些限制，我们引入了温度感知循环神经算子（TRNO），这是一种与时间分辨率无关的神经架构。 我们将TRNO应用于建模多晶镁的温度依赖塑性响应，该材料表现出强烈的塑性各向异性和热敏感性。 TRNO实现了高预测精度，并在各种载荷情况、温度和时间分辨率下具有良好的泛化能力。 它在训练效率和预测性能方面也优于传统的GRU和LSTM模型。 最后，我们展示了使用TRNO的多尺度模拟，其速度比传统本构模型提高了至少三个数量级。 显示英文摘要

摘要： Neural network surrogate models for constitutive laws in computational mechanics have been in use for some time. In plasticity, these models often rely on gated recurrent units (GRUs) or long short-term memory (LSTM) cells, which excel at capturing path-dependent phenomena. However, they suffer from long training times and time-resolution-dependent predictions that extrapolate poorly. Moreover, most existing surrogates for macro- or mesoscopic plasticity handle only relatively simple material behavior. To overcome these limitations, we introduce the Temperature-Aware Recurrent Neural Operator (TRNO), a time-resolution-independent neural architecture. We apply the TRNO to model the temperature-dependent plastic response of polycrystalline magnesium, which shows strong plastic anisotropy and thermal sensitivity. The TRNO achieves high predictive accuracy and generalizes effectively across diverse loading cases, temperatures, and time resolutions. It also outperforms conventional GRU and LSTM models in training efficiency and predictive performance. Finally, we demonstrate multiscale simulations with the TRNO, yielding a speedup of at least three orders of magnitude over traditional constitutive models.