SURFNet: Super-Resolution of Turbulent Flows with Transfer Learning using Small Datasets

Deep Learning (DL) algorithms are emerging as a key alternative to computationally expensive CFD simulations. However, state-of-the-art DL approaches require large and high-resolution training data to learn accurate models. The size and availability of such datasets are a major limitation for the de...

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Veröffentlicht in:	2021 30th International Conference on Parallel Architectures and Compilation Techniques (PACT) S. 331 - 344
Hauptverfasser:	Obiols-Sales, Octavi, Vishnu, Abhinav, Malaya, Nicholas P., Chandramowlishwaran, Aparna
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.09.2021
Schlagworte:	computational fluid dynamics Computational modeling Data collection deep learning Geometry ML for science super-resolution Superresolution Training Training data Transfer learning turbulent flows
Online-Zugang:	Volltext
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