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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
121 views
A Mathematical Analysis of Neural Operator Behaviors
Neural operators have emerged as transformative tools for learning mappings between infinite-dimensional function spaces, offering useful applications...
Oct 28, 2024
2 authors
74 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 are usually very expensive. Physics-informed neural operator (...
Nov 7, 2024
6 authors
116 views
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