Physics-informed neural operator for parametric phase-field modelling of interfacial degradation and microstructural evolution
Tongji University, College of Civil Engineering（同济大学，土木工程学院）；Tongji University, State Key Laboratory of Disaster Reduction in Civil Engineering（同济大学，灾害防治工程重点实验室）
Predicting interfacial degradation and microstructural evolution through phase-field modelling is computationally intensive, particularly for parametric studies. While neural operators such as the Fourier neural operator (FNO) offer a promising route to acceleration, the absence of physical constraints compromises their generalisation and long-term stability. Here, we present PF-PINO, a physics-informed neural operator framework that embeds phase-field governing equation residuals directly into the training loss, together with gradient normalisation loss balancing and a staggered training scheme for coupled multi-field systems. PF-PINO is validated on four benchmarks including pencil-electrode corrosion, electro-polishing corrosion, dendritic solidification, and spinodal decomposition, collectively probing parametric generalisation across orders-of-magnitude kinetic regimes, non-periodic boundaries with stochastic morphologies, coupled multi-field dynamics with out-of-distribution extrapolation, and long-term autoregressive stability under complex pattern formation. Across all benchmarks, PF-PINO reduces relative $L^2$ errors by up to an order of magnitude over FNO and achieves sub-mesh-size interface accuracy, establishing a data-efficient surrogate for high-throughput parametric phase-field assessment in computational materials science and engineering applications.
SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling
Department of Mechanical Engineering, University of Wisconsin-Madison（威斯康星大学麦迪逊分校机械工程系）；Department of Mathematics, University of Wisconsin-Madison（威斯康星大学麦迪逊分校数学系）；Department of Civil Engineering, Johns Hopkins University（约翰霍普金斯大学土木工程系）；Materials Physics and Applications Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室材料物理与应用 division）；Department of Materials Science and Engineering, University of Wisconsin-Madison（威斯康星大学麦迪逊分校材料科学与工程系）
Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed Neural Networks Framework for Interface Problems
Department of Civil Engineering, Indian Institute of Technology, Madras, Chennai 600036, Tamil Nadu, India（印度理工学院马德拉斯分校土木工程系）；Atmospheric, Earth and Energy Division, Lawrence Livermore National Laboratory, Livermore, California 94551, USA（劳伦斯利弗莫尔国家实验室大气、地球与能源部）；Institute of Civil Engineering, École Polytechnique Fédérale de Lausanne, Station 18-1015, Lausanne, Switzerland（洛桑联邦理工学院土木工程学院）
A physics-aware deep learning model for shear band formation around collapsing pores in shocked reactive materials
School of Data Science, University of Virginia（数据科学学院，弗吉尼亚大学）；Department of Mechanical Engineering, University of Iowa（机械工程系，爱荷华大学）；Department of Mechanical and Aerospace Engineering, University of Virginia（机械与航空航天工程系，弗吉尼亚大学）
Modeling and Inverse Identification of Interfacial Heat Conduction in Finite Layer and Semi-Infinite Substrate Systems via a Physics-Guided Neural Framework
Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials
School of Civil Engineering, Tongji University（同济大学土木工程学院）；The State Key Laboratory on Disaster Reduction in Civil Engineering, Tongji University（同济大学土木工程防灾国家重点实验室）
Adaptive Uncertainty-Guided Surrogates for Efficient phase field Modeling of Dendritic Solidification
Intelligent Information Systems, Tekniker（智能信息系统，Tekniker）；Department of Computer Sciences and Artificial Intelligence, UPV-EHU（计算机科学与人工智能系，UPV-EHU）；Advanced Manufacturing Technologies, Tekniker（先进制造技术，Tekniker）
Towards Spatio-Temporal Extrapolation of Phase-Field Simulations with Convolution-Only Neural Networks
Sandia National Laboratories（桑迪亚国家实验室）；Lawrence Berkeley National Laboratory（伯克利劳伦斯国家实验室）；University of California, Berkeley（加州大学伯克利分校）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
