Original paper
A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations
Abstract
Within the framework of parameter dependent Partial Differential Equations (PDEs), we develop a constructive approach based on Deep Neural Networks for the efficient approximation of the parameter-to-solution map. The research is motivated by the limitations and drawbacks of state-of-the-art algorithms, such as the Reduced Basis method, when addressing problems that show a slow decay in the Kolmogorov ...
Paper Details
Title
A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations
Published Date
Nov 16, 2022
Journal
Volume
92
Issue
340
Pages
483 - 524
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Notes
History