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状态表面的联合学习方程与不确定性意识的物理调节的神经网络
Dongyang Kuang1,2, Shiwei Li3, Buxuan Wang4
1Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519082, China. kuangdy@mail.sysu.edu.cn.
Scientific reports
|July 27, 2025
概括
基于物理学的深度学习方法EOSNN在极端条件下准确地模拟材料行为,优于传统和高斯过程方法. 它处理不确定性并改善预测,即使数据有限.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 在不同压力-温度-体积 (P-T-V) 条件下了解材料的行为在许多科学领域至关重要.
- 传统的状态方程 (EOS) 模型面临由于热力学假设和专家知识要求的局限性.
- 现有的机器学习方法,如高斯过程,与可扩展性,推断和内核选择灵敏度作斗争.
研究的目的:
- 引入EOSNN,一种基于神经网络的新型,基于物理的深度学习方法,用于学习多个EOS表面.
- 开发一个概率模型来量化EOS预测中的 aleatoric 和 epistemic 不确定性.
- 为了证明EOSNN在准确性和灵活性方面比传统和现有的机器学习方法更优越.
主要方法:
- EOSNN从各种数据中共同学习多个EOS表面,包括静态/动态压缩和ab initio计算.
- 整合了一个概率框架,以捕捉 aleatoric 和 epistemic 不确定性.
- 基于物理学的规范化 (例如,热容量,格鲁尼森参数,散装模量) 用于增强物理一致性.
主要成果:
- 在精度,灵活性和可扩展性方面,EOSNN显著优于传统和高斯工艺方法.
- 在一项具有挑战性的部分监督任务中,EOSNN实现了0.83的R2得分和0.52 eV/原子的RMSE,用于Hugoniot以外的能量预测.
- 基于物理学的规范化进一步提高了准确性,在特定情况下超过了传统的完全监督方法.
结论:
- EOSNN提供了一种强大而准确的方程状态建模方法,克服了现有方法的局限性.
- 基于物理学的深度学习和概率不确定性量化的整合为材料科学研究提供了强大的工具.
- EOSNN在各种条件下需要精确的材料行为预测的各种应用中展示了显著的潜力.
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