@article{20df7e0dd5224eefa383934328df64b8,
title = "A Dual Neural Network Approach to Topology Optimization for Thermal-Electromagnetic Device Design",
abstract = "Topology optimization for engineering problems often requires multiphysics (dual objective functions) and multi-timescale considerations to be coupled with manufacturing constraints across a range of target values. We present a dual neural network approach to topology optimization to optimize a 3-dimensional thermal-electromagnetic device (optical shutter) for maximum temperature rise across a range of extinction ratios while also considering manufacturing tolerances. One neural network performs the topology optimization, allocating material to each sub-pixel within a repeating unit cell. The size of each sub-pixel is selected with manufacturing considerations in mind. The other neural network is trained to predict performance of the device using extinction ratio and temperature rise over a given time period. Training data is generated using a finite element model for both the electromagnetic wave frequency domain and thermal time domain problems. Optimized designs across a range of targets are shown.",
keywords = "Electromagnetics, Inverse design, Machine learning, Neural networks, Topology optimization",
author = "Jasperson, {Benjamin A.} and Wood, {Michael G.} and Johnson, {Harley T.}",
note = "This material is based in part upon work supported by the National Science Foundation under Grant No. 1922758. This work made use of the Illinois Campus Cluster, a computing resource that is operated by the Illinois Campus Cluster Program (ICCP) in conjunction with the National Center for Supercomputing Applications (NCSA) and which is supported by funds from the University of Illinois at Urbana-Champaign. This work was also supported by the Laboratory Directed Research and Development Program at Sandia National Laboratories. This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC under Contract No. DE-NA0003525 with the U.S. Department of Energy (DOE). The employee owns all right, title and interest in and to the article and is solely responsible for its contents. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this article or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan https://www.energy.gov/downloads/doe-public-access-plan. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. This material is based in part upon work supported by the National Science Foundation under Grant No. 1922758 . This work made use of the Illinois Campus Cluster, a computing resource that is operated by the Illinois Campus Cluster Program (ICCP) in conjunction with the National Center for Supercomputing Applications (NCSA) and which is supported by funds from the University of Illinois at Urbana-Champaign . This work was also supported by the Laboratory Directed Research and Development Program at Sandia National Laboratories. This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC under Contract No. DE-NA0003525 with the U.S. Department of Energy (DOE) . The employee owns all right, title and interest in and to the article and is solely responsible for its contents. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this article or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan https://www.energy.gov/downloads/doe-public-access-plan . This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.",
year = "2024",
month = mar,
doi = "10.1016/j.cad.2023.103665",
language = "English (US)",
volume = "168",
journal = "CAD Computer Aided Design",
issn = "0010-4485",
publisher = "Elsevier Ltd",
}