Technical Papers
Mar 30, 2023

Accelerated Design of Architected Materials with Multifidelity Bayesian Optimization

Publication: Journal of Engineering Mechanics
Volume 149, Issue 6

Abstract

In this work, we present a multifidelity Bayesian optimization framework for designing architected materials with optimal energy absorption during compression. Data from both physical experiments (high fidelity) and numerical simulations (low fidelity) are fed in parallel to train the surrogate model, which iteratively decides the next sets of experiments and simulations to run in order to find the optimal structural parameters. We show that having multifidelity data sources allows the optimization framework to find the optimum after fewer iterations relative to using a single high-fidelity source. This saves both material costs and time in the optimization process. Finally, we also apply constraints (on relative density and stress variations) to the optimization process, finding optimal structures within the bounds of the constraints. This framework can be translated to other problems that require complex, high-fidelity, labor-intensive experiments while automating low-fidelity simulations.

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Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

CM and JRR gratefully acknowledge support via the University of Pennsylvania Materials Research Science and Engineering Center (MRSEC) (NSF DMR-1720530), AFOSR Grant No. FA9550-22-1-0163, and by a 3M Non-Tenured Faculty Award. PP gratefully acknowledges support via DOE Grant No. DE-SC0019116.

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Go to Journal of Engineering Mechanics
Journal of Engineering Mechanics
Volume 149Issue 6June 2023

History

Received: Oct 28, 2022
Accepted: Dec 12, 2022
Published online: Mar 30, 2023
Published in print: Jun 1, 2023
Discussion open until: Aug 30, 2023

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Dept. of Mechanical Engineering and Applied Mechanics, Univ. of Pennsylvania, Philadelphia, PA 19104. ORCID: https://orcid.org/0000-0002-8960-2467. Email: [email protected]
Paris Perdikaris, Ph.D. [email protected]
Professor, Dept. of Mechanical Engineering and Applied Mechanics, Univ. of Pennsylvania, Philadelphia, PA 19104. Email: [email protected]
Professor, Dept. of Mechanical Engineering and Applied Mechanics, Univ. of Pennsylvania, Philadelphia, PA 19104 (corresponding author). ORCID: https://orcid.org/0000-0001-5329-9980. Email: [email protected]

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