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Found 5 papers, 1 papers with code
Finite Expression Methods for Discovering Physical Laws from Data
Nonlinear dynamics is a pervasive phenomenon observed in scientific and engineering disciplines.
Deep Operator Learning Lessens the Curse of Dimensionality for PDEs
Deep neural networks (DNNs) have achieved remarkable success in numerous domains, and their application to PDE-related problems has been rapidly advancing.
Deep learning-based rapid generation of broadly reactive antibodies against SARS-CoV-2 and its Omicron variant
The COVID-19 pandemic has been ongoing for nearly two and half years, and new variants of concern (VOCs) of SARS-CoV-2 continue to emerge, which urges the development of broadly neutralizing antibodies.
Reproducing Activation Function for Deep Learning
We propose reproducing activation functions (RAFs) to improve deep learning accuracy for various applications ranging from computer vision to scientific computing.
Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning
This paper proposes Friedrichs learning as a novel deep learning methodology that can learn the weak solutions of PDEs via a minmax formulation, which transforms the PDE problem into a minimax optimization problem to identify weak solutions.
