Upcoming Events
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.
Kevin Moon's Science Unwrapped Talk
Conference/Seminar | Science Unwrapped
Seeing Science: Using machine learning to visualize data.
Data Science Club featuring Steve Bunnell
Panel Discussion/Presentation
Join us on September 21st for a presentation on Best Practices in Data Analytics!
Steve Bunnell has more than 25 years experience as an analyst and leader of analytics teams, working with companies such as American Express, eBay,and United Health Group.
Pure Math Seminar
Panel Discussion/Presentation
Pure Math Faculty Seminar is a series of talks by faculty members in pure math at USU and other institutions. The topics cover many areas of pure math including analysis, geometry, topology, algebra, and discrete math. The talks can be about the speakers’ research or surveys of general research areas.
Interested faculty and students from all disciplines are invited to participate in these talks.
Contact Zhaohu Nie (zhaohu.nie@usu.edu) for further information.
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 ...
Data Science & Statistics Seminar
Panel Discussion/Presentation
The Data Science and Statistics Seminar is a monthly series of talks involving current research by data scientists and statisticians from USU and other institutions. Faculty and students from all disciplines are invited to participate in these talks, which tend to involve interdisciplinary applications and methodological developments. Fall 2021 talks will be conducted remotely.
Math & Stats Education Teaching Showcase
Panel Discussion/Presentation
The Mathematics and Statistics Education Group hosts a monthly Teaching Showcase to collaborate and share ideas for strategic motivation and improvement in their teaching opportunities. Please consider sharing a short presentation or story about something you’ve tried in your classes.
Contact Pata Rujirawat 797-0746 if you'd be interested in presenting or for further information.
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