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A deformation-based framework for learning solution mappings of PDEs defined on varying domains
In this work, we establish a deformation-based framework for learning solution mappings of PDEs defined on varying domains. The union of functions def...
Dec 2, 2024
3 authors
563 views
Temporal Neural Operator for Modeling Time-Dependent Physical Phenomena
Neural Operators (NOs) are machine learning models designed to solve partial differential equations (PDEs) by learning to map between function spaces....
Apr 28, 2025
2 authors
204 views
LESnets (Large-Eddy Simulation nets): Physics-informed neural operator for large-eddy simulation of turbulence
Acquisition of large datasets for three-dimensional (3D) partial differential equations (PDE) is usually very expensive. Physics-informed neural opera...
Nov 7, 2024
6 authors
679 views
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