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physics.comp-ph2020
Multi-fidelity Generative Deep Learning Turbulent Flows
Nicholas Geneva, Nicholas Zabaras
In computational fluid dynamics, there is an inevitable trade off between accuracy and computational cost. In this work, a novel multi-fidelity deep generative model is introduced…
physics.comp-ph2019
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks
Nicholas Geneva, Nicholas Zabaras
In recent years, deep learning has proven to be a viable methodology for surrogate modeling and uncertainty quantification for a vast number of physical systems. However, in their…