SBIR Phase I: Neural Component Architecture to Accelerate Modeling & Simulation

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will result from enabling significant cost and time savings in developing new, more efficient designs in broad fields such as engineering and healthcare. If successful, the project will enable simulations of everything from automobiles to aerospace components and pharmaceuticals to run up to 100 times faster by representing a physical component of a system with an advanced digital analogue. To date, software incompatibilities have limited the development of this kind of modeling. This project will solve this problem through advanced computational and compiler techniques, and thereby demonstrate the feasibility of a new kind of design process with significant cost reductions. This Small Business Innovation Research Phase I project will demonstrate the feasibility of using neural components in a modular system. We will combine the successes of surrogate model optimization and neural ODEs to allow for component-based differential-algebraic equation models with automated model order reduction through a latent diffeq. The idea is to build complex models as an assembly of modular pre-designed simulation components using our recent advances in differential programming and learning software to allow for automated training of neural model order reduction for accelerating the solution of large acausal models. Two machine learning methods have promising prospects for accelerating traditional mechanistic modeling workflows: surrogate optimization and neural differential equations. In this project, we will integrate these components into a prototype system. 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.