English(EN) Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves

新AI框架可预测材料冲击波行为并量化不确定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-05 04:00

研究人员开发了一种新颖的物理约束高斯过程回归框架，用于预测冲击波Hugoniot曲线。该方法使用有限数量的冲击波模拟来准确估计极端条件下的材料行为并量化预测不确定性。该方法在碳化硅上进行了演示，可为材料科学的未来实验和模拟提供信息。 AI

影响引入了一种新颖的AI驱动材料科学模拟方法，有望加速发现并降低计算成本。

排序理由这是一篇详细介绍预测材料特性的新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · George D. Pasparakis, Himanshu Sharma, Rushik Desai, Chunyu Li, Alejandro Strachan, Lori Graham-Brady, Michael D. Shields · 2026-06-05 04:00

物理约束高斯过程预测冲击波Hugoniot曲线

arXiv:2601.06655v2 Announce Type: replace-cross Abstract: A physics-constrained Gaussian Process regression framework is developed for predicting shocked material states and their associated uncertainties along the Hugoniot curve using data from a small number of shockwave simula…