Schedule
Breakfast and registration. 8:00am-9:00am
Session 1. Chair: Kevin Carlberg, SNL
9:00am-9:45am: Alexandre Chorin, LBNL. “Data-based dimension reduction and stochastic parametrization in context”
9:45am-10:15am: Kevin Lin, University of Arizona. “Mori-Zwanzig formalism and discrete-time stochastic parameterization of chaotic dynamics”
10:15am-10:35am: Matthew J. Zahr, LBNL. “Efficient PDE-constrained optimization under uncertainty using adaptive model reduction and sparse grids”
10:35am-10:55am: Juliane Mueller, LBNL. “Surrogate models in computationally expensive optimization”
Coffee Break. 10:55am-11:10am
Session 2. Chair: Matthew Zahr, LBNL
11:10am-11:55am: Masayuki Yano, University of Toronto. “Model reduction of parametrized nonlinear PDEs: empirical quadrature procedure”
11:55am-12:40pm: Steve Brunton, University of Washington. “Data-driven discovery and control of nonlinear systems”
12:40am-1:00pm: Youngsoo Choi, SNL. “Space–time least-squares Petrov–Galerkin projection for nonlinear model reduction”
Lunch break. 1:00pm-2:00pm
Discussion topic 1: Machine learning and model reduction
Discussion topic 2: The role of real-world data in model reduction methods
Discussion topic 3: Open source software for model order reduction
Discussion topic 4: Real world applications of model reduction
Session 3. Chair: Alexandre Chorin, LBNL
2:00pm-2:45pm: Charbel Farhat, Stanford University. “A feasible mathematical framework for modeling and quantifying model-form uncertainties based on stochastic reduced-order bases”
2:45pm-3:15pm: Louis Durlofsky, Stanford University. “POD-TPWL and POD-DEIM reduced-order modeling of subsurface flow problems”
3:15pm-3:35pm: Geoffrey Oxberry, LLNL. “libROM: A distributed-memory adaptive incremental Proper Orthogonal Decomposition”
Coffee Break. 3:35pm-3:50pm
Session 4. Chair: Charbel Farhat, Stanford
3:50pm-4:35pm: Doug James, Stanford University. “Pros and cons of reduced-order modeling for physics-based sound synthesis”
4:35pm-5:05pm: J Nathan Kutz, University of Washington. “Data-driven discovery and sparse sampling for non-intrusive, online parametric reduced order models”
5:05pm-5:25pm: Liqian Peng, SNL. “Structure-preserving model reduction for marginally stable LTI systems”
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