2017 WCRW
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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”