Discussion of “Experimental Design and Modeling for Forward-Inverse Maps” by R. Barton & M. Morris, appearing in Technometrics
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UTCH_A_2512261_O.pdf
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Author(s)
Marzouk, Youssef M.
Date Issued
May 20, 2025
Journal
Technometrics
Publisher
Taylor & Francis
Citation
Marzouk, Y. (2025). Discussion of “Experimental Design and Modeling for Forward-Inverse Maps” by R. Barton & M. Morris, appearing in Technometrics . Technometrics, 67(3), 391–393.
Version
Final published version
Abstract
Inverse design—essentially the problem of finding system parameter values that achieve a given performance metric—is an enormously important problem across a wide range of engineering fields. Typical methods for inverse design employ a forward model for example, a complex computer simulation mapping design parameters to performance metrics, and embed it in an optimization loop. If the forward model is a black box, for which direct evaluation of gradients is intractable, then one must resort to derivative-free or so-called “zeroth order” optimization approaches (e.g., Močkus Citation1975; Jones, Schonlau, and Welch Citation1998; Conn, Scheinberg, and Vicente Citation2009; Larson, Menickelly, and Wild Citation2019). Most of these approaches iteratively construct a metamodel for the forward map during optimization. Barton and Morris (henceforth “the authors” or “BM”) propose instead to build an inverse metamodel, that is, a computationally inexpensive approximation of the performance metric-to-parameter map. The promise of such an inverse metamodel is that it makes inverse design much faster and more direct, bypassing the need for explicit optimization.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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DOI of Published Version
https://doi.org/10.1080/00401706.2025.2512261
