This grant supports a workshop to explore the use of machine learning with experimental mechanics and materials. The rapid development of new materials with complex processing and structure, along with the emergence of new and potent computer-assisted experimental methods for materials and mechanical characterization have led to challenges in effectively and efficiently analyzing very large data sets in order to determine the important parameters that control the overall mechanical behavior. While established and detailed methodologies, grounded on materials physics and mechanics, serve as the foundation to evaluate and design new classes of materials with desirable mechanical properties, Data Science driven approaches for rapid assessment of important problem parameters could accelerate materials development and facilitate transitions through rapid processes for identifying structure-mechanical properties relationships from large experimental and modeling data sets. Such capabilities can impact new and emerging manufacturing methods, e.g. additive manufacturing, help us to understand complex processes in biological material systems, and finally accelerate the design of mechanically robust material systems.
This workshop aims at connecting mechanicians with researchers in the Data Sciences field for a dialogue that could open new avenues in the field of mechanics of materials, with special emphasis on the application of machine learning to experimental mechanics. It will bring together researchers who engage in new, multimodal, experimental methods in mechanics, with early adopters of machine learning tools in the fields of materials and mechanics, and leaders from the machine learning community. The workshop aims at developing a long term perspective for the introduction of Data Science methods to the field of mechanics of materials. Therefore, a specific aim is to assess the potential and the limitations of machine learning techniques in providing guidance and enhanced capabilities to quantify the contribution of the, often numerous and coupled, parameters governing the mechanics of complex materials and systems.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
