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September 2021

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13
Sep

Hybrid Applied Mathematics Seminar

Conference/Seminar

Talk Title: Solving Heat Transfer and Fluid Flow Problems with Reduced Order Models and Neural Networks

Speaker: Dr. Zilong Song Department of Math & Stat, USU

Abstract: The friction stir welding process can be modelled using a system of heat transfer and Navier-Stokes equations with a shear dependent viscosity. Finding numerical solutions of this system of nonlinear partial differential equations over a set of parameter space, however, is extremely time-consuming. Therefore, it is desirable to find a computationally efficient method that can be used to obtain an approximation of the solution with acceptable accuracy. In this talk, we present a reduced basis method for solving the parametrized coupled system of heat and Navier-Stokes equations using a proper orthogonal decomposition (POD). In addition, we apply a machine learning algorithm based on an artificial neural network (ANN) to learn (approximately) the relationship between relevant parameters and the POD coefficients. Our computational experiments demonstrate that substantial speed-up can be achieved while maintaining reasonable accuracy.

10:30 am - 11:30 am | Animal Science |
17
Sep

Kevin Moon's Science Unwrapped Talk

Conference/Seminar | Science Unwrapped

Seeing Science: Using machine learning to visualize data.

7:00 pm - 8:00 pm | Eccles Science Learning Center Auditorium |
27
Sep

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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