College of Engineering, Design & Computing Events

15 Jan | Integrating Machine Learning with Computational Fluid Dynamics Models of Orally Inhaled Drug Products (U01) Clinical Trials Not Allowed

NIH

Despite the advances in the power of modern computers, there are still some bottlenecks in using computational fluid dynamics (CFD) due to computational time, limited grid resolution, pre- and post-processing of large simulation data sets, model parameter estimations, and uncertainty quantifications. Machine learning (ML) has been gaining more attention as a potential tool to alleviate such limitations that arise in CFD. The purpose of this grant is to develop a methodology to integrate ML with CFD models of generically orally inhaled drug products (OIDP) to promote alternative bioequivalence studies to enhance and accelerate the development and approval of generic OIDPs.