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Using Simulation to Accelerate Autonomous Experimentation (AE): A Case Study using Mechanics

Aldair E. Gongora
Elise F. Morgan
Emily Whiting
Keith A. Brown
Kelsey Snapp
Kristofer Reyes
Patrick Francis Riley
iScience (2021), pp. 102262
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Autonomous experimentation (AE) accelerates research by combining automation and machine learning to perform experiments intelligently and rapidly in a sequential fashion. While AE systems are most needed to study properties that cannot be predicted analytically or computationally, even imperfect predictions can in principle be useful. Here, we use a case study on the mechanics of additively manufactured polymer structures to investigate whether imperfect data from simulation can accelerate AE. Initially, we study resilience, a property that is well- predicted by finite element analysis (FEA), and find that FEA can be used to build a Bayesian prior, and that experimental data can be integrated using discrepancy modeling to reduce the number of needed experiments ten-fold. Next, we study toughness, which is not well predicted by FEA, and find that FEA can still improve learning by transforming experimental data and guiding experiment selection. These results highlight multiple ways in which simulation can improve AE through transfer learning.

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