2111638

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to improve utilization of data resources. While data is an integral part of contemporary business—used to inform strategic, technical, and financial decisions—data collection remains federated in many fields, including manufacturing, because logistical, practical, and strategic hurdles prevent centralization. Consequently, these data resources quickly become isolated. No longer FAIR (Findable, Accessible, Interoperable, Reusable), the value of data so expensive to collect is lost. The proposed technology addresses two major concerns facing effective utilization of federated data. First, it develops a unified interface to analyze and explore federated data, without sacrificing control over data access. Second, it integrates machine learning with an understanding of the physical system. The proposed technology is a mathematically rigorous translation between neural networks and the constitutive relationships describing the underlying physics. The two approaches will leverage measurements of a process environment, including time, temperature, and pressure, as well as mechanical strength or chemical reactivity. Neural networks, which are general and easy to train, estimate system behavior through statistical correlations, which is ideal for repetitive, complex systems, such as manufacturing processes; but they require increasingly large and diverse datasets to expand the conditions under which they are reliable. In contrast, constitutive relationships, which often take years to develop, can be used to predict how a system will behave under new conditions. This system will integrate both approaches. 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.