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