Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems

Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems

Abstract Physical systems whose dynamics are governed by partial differential equations (PDEs) find numerous applications in science and engineering. The process of obtaining the solution from such PDEs may be computationally expensive for large-scale and parameterized problems. In this work, deep l...

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Bibliographic Details
Main Authors:	Pratyush Bhatt, Yash Kumar, Azzeddine Soulaïmani
Format:	Article
Language:	English
Published:	SpringerOpen 2023-11-01
Series:	Advanced Modeling and Simulation in Engineering Sciences
Subjects:	Non-intrusive reduced-order modeling Deep autoencoders LSTM TCN CNN Time-dependent flow problems
Online Access:	https://doi.org/10.1186/s40323-023-00254-y

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