A Machine Learning Approach to Unresolved-Scale Modeling for Burgers’ Equation
Michel Robijns (TU Delft - Aerospace Engineering)
Steven Hulshoff – Mentor (TU Delft - Aerodynamics)
Richard Dwight – Coach (TU Delft - Aerodynamics)
B. Y. Chen – Coach (TU Delft - Aerospace Structures & Computational Mechanics)
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Abstract
This thesis is part of a greater effort to use machine learning for the development of flexible and universal unresolved-scale models in large eddy simulation (LES). The novelty in the current work is that a neural network learns to predict the integral forms of the unresolved-scale terms directly without a priori assumptions on the underlying functional relationship. The contribution of this thesis is a validation of a neural-network-based unresolved-scale model for Burgers' equation which paves the way for future application to the Navier-Stokes equations.