SBIR Phase I: Automatic Generation of Physics-Informed AI Models

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to democratize the model building process for multiple industrial applications by: (1) making it easy to build models within hours instead of weeks, (2) cut model building costs by 10x or more, and (3) significantly mitigate risks by providing more accurate and interpretable models that are constrained by underlying physical principles. This technology would unlock a new generation of modeling workflows that are more scalable, have less uncertainty, and improve the structural visibility and interoperability of complex processes that are partially understood. As a result, companies would have tangible ways to reduce modeling costs, streamline optimization and control of operations and ultimately, achieve better decisions at economically viable rates. More broadly, the proposed technology should inspire various innovations and enhancements to products and services that strongly rely on physics modeling. It is expected that tangible results would induce profound effects across multiple sectors, including agriculture, environmental science, civil engineering, manufacturing, aerospace, construction, logistics, and medicine. This Small Business Innovation Research (SBIR) Phase I project will set the technical and business foundations required to automate and expedite the construction of reduced physics models from data with minimal human intervention. The goal for this project is to implement fast and reliable mechanisms for: (1) mapping spatio-temporal data of the physical world into structural network representations; (2) leverage the data from these network representations to generate a suitable set of equations that explain the underlying dynamics; (3) use advanced optimization techniques to find a list of models that capture the best combination of network and equations matching the observed data; and (4) orchestrate all these pieces into a single artificial intelligence platform that automatically consolidates data and human interactions for model building and visualization. Efforts in Phase I are particularly focused on demonstrating that the resulting models are physically sound and sufficiently robust for describing the dynamics of fluid transport and diffusion based on different amounts of available data and conditions. 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.