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Applied Mathematics Seminar

Conference/Seminar

In-Person: ANSC 119
ZOOM ID: 860 6092 4997 PWD: USUAMS

Title: Sensitivity analysis and uncertainty quantification using novel physics-constrained machine learning

Speaker: Dr. Hamidreza Karbasian, Fields Institute, University of Toronto

Abstract:
A new physics-constrained data-driven approach is proposed for sensitivity analysis and uncertainty quantification of large-scale chaotic dynamical systems. Unlike conventional sensitivity analysis, this approach can solve the unsteady sensitivity function for PDE-constrained optimizations. In this new approach, high-dimensional governing equations, such as Navier-Stokes equations, from physical space are transformed into an unphysical space to develop a closure model in the form of a Reduced-Order Model (ROM). Subsequently, a new data sampling approach is proposed to build a data-driven approach for this framework. It is shown that the proposed approach can capture sensitivities for large-scale chaotic dynamical systems, where Finite Difference (FD) approximations fail ...

10:30 am - 11:30 am | Animal Science |
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