Data-Driven Methods for Reduced-Order Modeling and Stochastic Partial Differential Equations
Videos from BIRS Workshop
Bing Brunton, University of Washington
Monday Jan 30, 2017 09:09 - 09:57
Sparse sensing and dimensionality reduction in neuroscience
Joshua L. Proctor, Institute for Disease Modeling
Monday Jan 30, 2017 09:59 - 10:39
Adapting equation-free modeling techniques for modern infectious disease data
Alessandro Alla, Florida State University
Monday Jan 30, 2017 11:17 - 11:58
Randomized model order reduction
Andrew Fitzgibbon, Microsoft
Monday Jan 30, 2017 14:04 - 15:14
Modelling of 3D shape spaces from limited data
Harish S. Bhat, University of California, Merced
Tuesday Jan 31, 2017 11:42 - 12:23
Density Tracking by Quadrature for SDE Inference
Steve Brunton, University of Washington
Tuesday Jan 31, 2017 14:01 - 15:07
Data-Driven Discovery and Control of Nonlinear Dynamical Systems
Roy Lederman, Princeton
Tuesday Jan 31, 2017 16:21 - 16:39
A Representation Theory Perspective on Simultaneous Alignment and Classification
Sam Rudy, University of Washington
Tuesday Jan 31, 2017 16:40 - 17:01
Data-driven discovery of partial differential equations
Romeo Alexander, Courant Institute
Tuesday Jan 31, 2017 17:03 - 17:34
Kernel Analog Forecasting with Applications to Intra-seasonal Tropical Oscillations
James Kunert-Graf, Pacific Northwest Research Institute
Tuesday Jan 31, 2017 17:46 - 18:06
Dynamic Mode Decomposition Reveals Low-Dimensional Structure and a Hierarchy of Timescales in C. elegans Neural Dynamics
Yvon Maday, University Paris 6
Wednesday Feb 1, 2017 18:30 - 19:29
In pursuit of more reductions in RBM for convection type problems
Kevin Carlberg, Sandia National Laboratories
Wednesday Feb 1, 2017 19:31 - 19:57
Structure-preserving model reduction for nonlinear finite-volume models
Karthik Duraisamy, University of Michigan
Wednesday Feb 1, 2017 19:58 - 20:30
Closure Modeling for Reduced Order Models of Multi-Scale Problems
Sara Grundel, Max Planck Institute
Wednesday Feb 1, 2017 20:33 - 20:53
Parametric Model Order Reduction or Uncertainty Quantification?
Charbel Farhat, Stanford University
Thursday Feb 2, 2017 09:06 - 10:08
Innovative Model Reduction Based Computational Technologies for Complex Engineering Problems: Progress, Results and Challenges
Boris Kramer, Massachusetts Institute of Technology
Thursday Feb 2, 2017 10:09 - 10:39
Data-driven modeling for control of systems with time-varying and uncertain parameters
Matthew Zahr, Lawrence Berkeley National Laboratory
Thursday Feb 2, 2017 11:01 - 11:32
Efficient PDE-Constrained Optimization under Uncertainty using Adaptive Model Reduction and Sparse Grids
Kunihiko Taira, Florida State University
Thursday Feb 2, 2017 11:33 - 11:57
Network-based modeling and control of unsteady fluid flows
Serkan Gugercin, Virginia Polytechnic Institute
Thursday Feb 2, 2017 14:01 - 15:04
What to interpolate for optimal model reduction: Moving from linear to nonlinear dynamics
Matthew Williams, United Technologies Research Center
Thursday Feb 2, 2017 16:00 - 16:20
Koopman Meets Bellman: Another Route to Control Using Data-Driven Kooman Analysis
Eurika Kaiser, University of Washington
Thursday Feb 2, 2017 16:21 - 16:41
Sparse cluster-based reduced order modeling
Travis Askham, University of Washington
Thursday Feb 2, 2017 16:43 - 17:03
An extension of the dynamic mode decomposition and applications
Krithika Manohar, University of Washington
Thursday Feb 2, 2017 17:04 - 17:21
Data-driven Sparse Sensor Placement
Laura Slivinski, NOAA
Thursday Feb 2, 2017 17:22 - 17:42
Opportunities for Improvement in the Twentieth Century Reanalysis
Martin Grepl, Aachen
Friday Feb 3, 2017 09:02 - 10:11
Certified Reduced Basis Methods for Optimal Control and Data Assimilation
