用于温度依赖热电性质可泛化且无标签推断的物理信息神经算子
Physics-Informed Neural Operators for Generalizable and Label-Free Inference of Temperature-Dependent Thermoelectric Properties
AI总结：
提出融合PINN与PINO的物理信息机器学习框架，仅利用稀疏场数据即可实现温度依赖热电性质的无标签、跨材料泛化推断。
摘要：
准确表征热导率和塞贝克系数等随温度变化的热电性质（TEPs），对于热电装置的可靠建模和高效设计至关重要。然而，这些性质的非线性温度依赖和耦合输运行为使正演模拟和反演识别都很困难，尤其是在稀疏测量条件下。本研究开发了一种物理信息机器学习方法，采用物理信息神经网络（PINN）求解热电系统中的正问题和反问题，并使用神经算子（PINO）实现跨多种材料系统的泛化。PINN通过将控制输运方程嵌入损失函数来实现场重构和材料性质推断，而PINO则无需重新训练即可将这种推断能力泛化到多种材料。PINO模型在20种p型材料的模拟数据上训练，并在60种未见过的材料上评估，展示了仅使用稀疏场数据即可对TEPs进行准确且无标签的推断。所提出框架为热电性质识别提供了一种可扩展、可泛化且数据高效的方法，为先进热电材料的高通量筛选和逆向设计铺平了道路。
英文摘要：
Accurate characterization of temperature-dependent thermoelectric properties (TEPs), such as thermal conductivity and the Seebeck coefficient, is essential for reliable modeling and efficient design of thermoelectric devices. However, their nonlinear temperature dependence and coupled transport behavior make both forward simulation and inverse identification difficult, particularly under sparse measurement conditions. In this study, we develop a physics-informed machine learning approach that employs physics-informed neural networks (PINN) for solving forward and inverse problems in thermoelectric systems, and neural operators (PINO) to enable generalization across diverse material systems. The PINN enables field reconstruction and material property inference by embedding governing transport equations into the loss function, while the PINO generalizes this inference capability across diverse materials without retraining. Trained on simulated data for 20 p-type materials and evaluated on 60 unseen materials, the PINO model demonstrates accurate and label-free inference of TEPs using only sparse field data. The proposed framework offers a scalable, generalizable, and data-efficient approach for thermoelectric property identification, paving the way for high-throughput screening and inverse design of advanced thermoelectric materials.
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