Ali Mjalled, Reza Namdar, Lucas Reineking, Mohammad Norouzi, Fathollah Varnik, Martin Mönnigmann
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Flow field prediction in bed configurations: A parametric spatio-temporal convolutional autoencoder approach
The use of deep learning methods for modeling fluid flow has drawn a lot of attention in the past few years. Here we present a data-driven reduced-order model (ROM) for predicting flow fields in a ...
期刊介绍：
Published 12 times per year, Numerical Heat Transfer, Part B: Fundamentals addresses all aspects of the methodology for the numerical solution of problems in heat and mass transfer as well as fluid flow. The journal’s scope also encompasses modeling of complex physical phenomena that serves as a foundation for attaining numerical solutions, and includes numerical or experimental results that support methodology development.
All submitted manuscripts are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous expert referees. The Editor reserves the right to reject without peer review any papers deemed unsuitable.
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