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Data-Driven Model Reduction, Scientific Frontiers, and Applications

Description

Data-driven modeling is a cornerstone for many applications. Finding appropriate scale/level models conditioned to the data requires some type of reduced-order modeling. This workshop brings together experts working on mathematical, statistical, computational, and engineering aspects of model reduction to share their research experience.

The workshop will be hosted by Texas A&M University in College Station, Texas, and is supported by the Institute for Scientific Computation, Texas A&M Energy Institute, and Foundation CMG.

We will provide and update information for this workshop online at http://isc.tamu.edu/events/ModRedApr2017/.

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Organizing Committee

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Speakers

Anirban Bhattacharya, Department of Statistics
Suman Chakravorty, Department of Aerospace Engineering
A Computationally Optimal Randomized Proper Orthogonal Decomposition Technique
Paul Cizmas, Department of Aerospace Engineering
A Proper Orthogonal Decomposition Method for Deforming Computational Domains
Yalchin Efendiev, Department of Mathematics
Some Concepts for Local Multiscale Model Reduction
Simon Foucart, Department of Mathematics
Computing a Quantity of Interest from Observational Data
Irina Gaynanova, Department of Statistics
Joint Exploratory Analysis of Multiple Heterogeneous Data Source
Richard Gibson, Department of Geology & Geophysics
Applications of Generalized Multiscale Finite Element Modeling to Efficient and Adaptive Seismic Imaging Techniques
Eduardo Gildin, Department of Petroleum Engineering
Model Reduction for Efficient Oil Reservoir Simulation and Production Optimization
Faruque Hassan, Department of Chemical Engineering
Data-Driven Feasibility Mapping and Optimization
Mike King, Department of Petroleum Engineering
Upscaling of Fluid Flow in High Contrast Systems
Costas Kravaris, Department of Chemical Engineering
Dynamic Model Reduction for Two-Stage Anaerobic Bioreactors
Joseph Kwon, Department of Chemical Engineering
Development of Local Model Reduction Technique: Application to Hydraulic Fracturing

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Schedule

All workshop presentations will take place in Rudder Tower (RDER) 510. Please note that the schedule is tentative and subject to change. All times listed are local time.

–
Welcome
–
Costas Kravaris, Department of Chemical Engineering
Dynamic Model Reduction for Two-Stage Anaerobic Bioreactors
–
Joseph Kwon, Department of Chemical Engineering
Development of Local Model Reduction Technique: Application to Hydraulic Fracturing
–
Suman Chakravorty, Department of Aerospace Engineering
A Computationally Optimal Randomized Proper Orthogonal Decomposition Technique
–
Irina Gaynanova, Department of Statistics
Joint Exploratory Analysis of Multiple Heterogeneous Data Source
–
Break
–
Eduardo Gildin, Department of Petroleum Engineering
Model Reduction for Efficient Oil Reservoir Simulation and Production Optimization
–
Anirban Bhattacharya, Department of Statistics
–
Lunch Break
–
Simon Foucart, Department of Mathematics
Computing a Quantity of Interest from Observational Data
–
Richard Gibson, Department of Geology & Geophysics
Applications of Generalized Multiscale Finite Element Modeling to Efficient and Adaptive Seismic Imaging Techniques
–
Faruque Hassan, Department of Chemical Engineering
Data-Driven Feasibility Mapping and Optimization
–
Break
–
Paul Cizmas, Department of Aerospace Engineering
A Proper Orthogonal Decomposition Method for Deforming Computational Domains
–
Mike King, Department of Petroleum Engineering
Upscaling of Fluid Flow in High Contrast Systems
–
Yalchin Efendiev, Department of Mathematics
Some Concepts for Local Multiscale Model Reduction
–
Closing Remarks

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Contact Information

If you have any questions concerning this workshop, email Brad Shumbera at shumbera@tamu.edu.

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