基于物理学的神经网络用于解决相场模型中的反向问题
1Key Laboratory of Structure and Thermal Protection of High Speed Aircraft, Ministry of Education, School of Mechanical Engineering, Southeast University, Nanjing, 211189, Jiangsu, China; Jiangsu Key Laboratory for Biomaterials and Devices, Southeast University, Dingjiaqiao 87, Nanjing, 210009, Jiangsu, China.
概括
这项研究使用物理信息神经网络 (PINNs) 来解决材料科学中的反向问题,揭示物理定律并准确地反转材料参数. 这种方法在异构和多物理系统中得到了验证.
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:
- 物理信息神经网络 (PINNs) 在材料科学中越来越多地使用.
- 当前的研究往往侧重于前性问题或预测准确性.
- 需要将PINNs应用于反向问题,以更深入地理解材料.
研究的目的:
- 在数值模拟建模中的反向问题上应用PINNs.
- 使用集成的神经网络从数据中发现潜在的物理定律.
- 为了验证PINN用于反转异性质材料参数和多物理系统.
主要方法:
- 开发了一个神经网络,集成数据驱动和物理驱动的模块.
- 在扩散,流量和相位过渡模拟中应用PINN来解决反向问题.
- 验证了对基准异构函数反转和多物理合系统的方法.
主要成果:
- 在模拟数据中成功发现嵌入的物理定律.
- 实现了对异型参数反转的预测值和理论值之间的高一致性.
- 证明了PINNs对多物理合系统 (相场,温度,流量) 的反向问题的适用性.
结论:
- 在材料科学模拟中,PINN对于解决反向问题是有效的.
- 综合方法可以逆转关键的异构和多物理材料参数.
- 这项工作突显了将数值模拟数据与深度学习相结合的潜力,以推进材料科学研究.
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