Reduced Order Modeling of a MOOSE-based Advanced Manufacturing Model
with Operator Learning
- URL: http://arxiv.org/abs/2308.09691v1
- Date: Fri, 18 Aug 2023 17:38:00 GMT
- Title: Reduced Order Modeling of a MOOSE-based Advanced Manufacturing Model
with Operator Learning
- Authors: Mahmoud Yaseen, Dewen Yushu, Peter German and Xu Wu
- Abstract summary: 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.
- Score: 2.517043342442487
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space [72.52365911990935]
We introduce Bellman Diffusion, a novel DGM framework that maintains linearity in MDPs through gradient and scalar field modeling.
Our results show that Bellman Diffusion achieves accurate field estimations and is a capable image generator, converging 1.5x faster than the traditional histogram-based baseline in distributional RL tasks.
arXiv Detail & Related papers (2024-10-02T17:53:23Z) - Deep Neural Operator Enabled Digital Twin Modeling for Additive Manufacturing [9.639126204112937]
A digital twin (DT) behaves as a virtual twin of the real-world physical process.
We present a deep neural operator enabled computational framework of the DT for closed-loop feedback control of the L-PBF process.
The developed DT is envisioned to guide the AM process and facilitate high-quality manufacturing.
arXiv Detail & Related papers (2024-05-13T03:53:46Z) - MFRL-BI: Design of a Model-free Reinforcement Learning Process Control
Scheme by Using Bayesian Inference [5.375049126954924]
Design of process control scheme is critical for quality assurance to reduce variations in manufacturing systems.
We propose a model-free reinforcement learning (MFRL) approach to conduct experiments and optimize control simultaneously according to real-time data.
arXiv Detail & Related papers (2023-09-17T08:18:55Z) - Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive
Manufacturing Model with Operator Learning [1.4528756508275622]
The present work is to construct a fast and accurate reduced-order model (ROM) for an additive manufacturing (AM) model.
We benchmarked the performance of these OL methods against a conventional deep neural network (DNN)-based ROM.
arXiv Detail & Related papers (2023-08-04T17:00:34Z) - End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures.
We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
arXiv Detail & Related papers (2023-05-25T10:58:46Z) - Predictable MDP Abstraction for Unsupervised Model-Based RL [93.91375268580806]
We propose predictable MDP abstraction (PMA)
Instead of training a predictive model on the original MDP, we train a model on a transformed MDP with a learned action space.
We theoretically analyze PMA and empirically demonstrate that PMA leads to significant improvements over prior unsupervised model-based RL approaches.
arXiv Detail & Related papers (2023-02-08T07:37:51Z) - SMPL: Simulated Industrial Manufacturing and Process Control Learning
Environments [26.451888230418746]
There is little exploration of applying deep reinforcement learning to control manufacturing plants.
We develop an easy-to-use library that includes five high-fidelity simulation environments.
We benchmark online and offline, model-based and model-free reinforcement learning algorithms for comparisons of follow-up research.
arXiv Detail & Related papers (2022-06-17T15:51:35Z) - A Reinforcement Learning-based Economic Model Predictive Control
Framework for Autonomous Operation of Chemical Reactors [0.5735035463793008]
This work presents a novel framework for integrating EMPC and RL for online model parameter estimation of a class of nonlinear systems.
The major advantage of this framework is its simplicity; state-of-the-art RL algorithms and EMPC schemes can be employed with minimal modifications.
arXiv Detail & Related papers (2021-05-06T13:34:30Z) - Learning Discrete Energy-based Models via Auxiliary-variable Local
Exploration [130.89746032163106]
We propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data.
We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration.
We present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
arXiv Detail & Related papers (2020-11-10T19:31:29Z) - VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven
Model Interpretability Applied to the Ironmaking Industry [70.10343492784465]
It is necessary to expose to the process engineer, not solely the model predictions, but also their interpretability.
Model-agnostic local interpretability solutions based on LIME have recently emerged to improve the original method.
We present in this paper a novel approach, VAE-LIME, for local interpretability of data-driven models forecasting the temperature of the hot metal produced by a blast furnace.
arXiv Detail & Related papers (2020-07-15T07:07:07Z) - Information Theoretic Model Predictive Q-Learning [64.74041985237105]
We present a novel theoretical connection between information theoretic MPC and entropy regularized RL.
We develop a Q-learning algorithm that can leverage biased models.
arXiv Detail & Related papers (2019-12-31T00:29:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.