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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
639 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
1232 views
Physics-informed Kolmogorov-Arnold Network with Chebyshev Polynomials for Fluid Mechanics
Solving partial differential equations (PDEs) is essential in scientific forecasting and fluid dynamics. Traditional approaches often incur expensive...
Nov 7, 2024
5 authors
844 views
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