| Abstract Scope |
Additive manufacturing (AM) has increased the complexity with which structures can be designed and fabricated. Computational tools, empowered by the control afforded by AM, have enabled the discovery and realization of structures with enhanced or tailored mechanical performance. However, this approach is limited to mechanical properties that can be reliably predicted using simulation. For properties that cannot be reliably simulated, such as toughness, autonomous experimental research platforms have emerged to explore the design space for high performing structures by combining automated experimentation and active learning. An open question that remains is how to effectively combine simulation, with varying degrees of accuracy and cost, and autonomous experimentation in order to accelerate learning. In this work, we evaluate a series of methods for combining simulation and autonomous experimentation. |