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Phase-Field DeepONet: Physics-informed deep operator neural network for fast simulations of pattern formation governed by gradient flows of free-energy functionals
Wei Li, Martin Z. Bazant, Juner Zhu
Northeastern University Massachusetts Institute of Technology
来源Computer Methods in Applied Mechanics and Engineering
年份2023
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物理Model Reduction and Neural Networks
Lattice Boltzmann Simulation Studies · Nanofluid Flow and Heat Transfer
参考文献 55
Theory of Chemical Kinetics and Charge Transfer based on Nonequilibrium Thermodynamics
被引 718Martin Z. Bazant · Accounts of Chemical Research · 2013
Linking anisotropic sharp and diffuse surface motion laws via gradient flows
被引 208Jean E. Taylor, John W. Cahn · Journal of Statistical Physics · 1994
Overview no. 113 surface motion by surface diffusion
被引 293John W. Cahn, Jean E. Taylor · Acta Metallurgica et Materialia · 1994
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引用本文 75
Understanding and design of metallic alloys guided by phase-field simulations
被引 237Yuhong Zhao · npj Computational Materials · 2023
Physics-informed machine learning in intelligent manufacturing: a review
被引 76Jiewu Leng, Kaiwen Zuo, Caiyu Xu · Journal of Intelligent Manufacturing · 2025
En-DeepONet: An enrichment approach for enhancing the expressivity of neural operators with applications to seismology
被引 53Ehsan Haghighat, Umair bin Waheed, George Em Karniadakis · Computer Methods in Applied Mechanics and Engineering · 2023
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