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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
64 views
A dual physics-informed neural network for topology optimization
We propose a novel dual physics-informed neural network for topology optimization (DPNN-TO), which merges physics-informed neural networks (PINNs) wit...
Oct 18, 2024
3 authors
187 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
229 views
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