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Reduced Order Modeling of a MOOSE-based Advanced Manufacturing Model with Operator Learning
The goal of this work is to develop an accurate and fast-running reduced order model (ROM) for this MOOSE-based AM model that can be used in a DRL-based process control and optimization method.
Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learning
More specifically, we used the Fourier neural operator (FNO) and deep operator network (DeepONet) to develop ROMs for time-dependent responses.
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