| Abstract Scope |
The combination of automated experimentation and active learning has resulted in autonomous systems that accelerate the pace of research in fields such as materials science, chemistry, biology, and mechanics. For instance, we have recently reported a Bayesian experimental autonomous researcher (BEAR) that combines 3D printing and automated testing to realize structural materials that have highly tuned non-linear mechanical performance. While this approach was initially a black-box process, mechanics insight in the form of finite element analysis (FEA) contains critical insight that can in principle be useful, despite not completely capturing experimental performance. Here, we describe recent efforts to make the BEAR physics-informed through the incorporation of FEA. The interplay of high fidelity experimentation and comparatively low fidelity but high throughput simulation makes this an interesting case study for how best to efficiently use these disparate data streams for structural optimization. |