Reduced Order Modeling of a MOOSE-based Advanced Manufacturing Model
  with Operator Learning
Mahmoud Yaseen Dewen Yushu Peter German Xu Wu
Advanced Manufacturing (AM) has gained significant interest in the nuclear
community for its potential application on nuclear materials. One challenge is
to obtain desired material properties via controlling the manufacturing process
during runtime. Intelligent AM based on deep reinforcement learning (DRL)
relies on an automated process-level control mechanism to generate optimal
design variables and adaptive system settings for improved end-product
properties. A high-fidelity thermo-mechanical model for direct energy
deposition has recently been developed within the MOOSE framework at the Idaho
National Laboratory (INL). 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. Operator
learning (OL)-based methods will be employed due to their capability to learn a
family of differential equations, in this work, produced by changing process
variables in the Gaussian point heat source for the laser. We will develop
OL-based ROM using Fourier neural operator, and perform a benchmark comparison
of its performance with a conventional deep neural network-based ROM.