Accelerated Design of Architected Materials with Multifidelity Bayesian Optimization

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.

Automated summary

In this work, a multifidelity Bayesian optimization framework is proposed for designing architected materials with optimal energy absorption during compression. The framework uses data from both physical experiments (high fidelity) and numerical simulations (low fidelity) to train a surrogate model, which guides the selection of the next experiments and simulations. The results show that the multifidelity approach reduces the number of iterations required to find the optimum and saves material costs and time in the optimization process. Constraints on relative density and stress variations are also incorporated into the optimization process to find optimal structures within the bounds of the constraints. This framework can be applied to other problems that involve complex, high-fidelity, labor-intensive experiments, while automating low-fidelity simulations.