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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 and Energi Simulation.

We will provide and update information for this workshop online at http://isc.tamu.edu/events/ModRed2018/.

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Organizing Committee

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Speakers

Ishan Bajaj, Artie McFerrin Department of Chemical Engineering
Computational Experience with Different Reduced Models for Derivative-Free Optimization
Chiranjivi Botre, Artie McFerrin Department of Chemical Engineering
Modified Nonlinear Partial Least Square (PLS) as a Model Reduction Technique with Applications to Fault Detection
Doowon Choi, Department of Industrial & Systems Engineering
Correlation Trees with Application to Neural Correlates Studies
Yalchin Efendiev, Department of Mathematics
Nonlocal Multicontinua Upscaling for Flows in Porous Media
Mahmood Ettehad, Department of Mathematics
Graph Reconstruction from Path Correlation Data
Horacio Florez, Harold Vance Department of Petroleum Engineering
Uncertainty Quantification and Model-Order Reduction on Linear and Nonlinear Problems: Analysis, Approaches, and Challenges
Irina Gaynanova, Department of Statistics
Structural Learning and Integrative Decomposition of Multi-View Data
Eduardo Gildin, Harold Vance Department of Petroleum Engineering
Faster Reservoir Simulation and Optimization using Control System Insights
Michael King, Harold Vance Department of Petroleum Engineering
Improved Localization in Upscaling of Fluid Flow in Porous Media
Abhinav Narasingam, Artie McFerrin Department of Chemical Engineering
Data-Driven Identification of Interpretable Reduced-Order Models Using Sparse Regression
Krishna Nunna, Harold Vance Department of Petroleum Engineering
Multiscale Reservoir Simulation Using Pressure Transient Concepts
Robert Skelton, Department of Aerospace Engineering
Modeling Dynamic Systems is Not Just About Physics: Toward the Marriage of Physics and Signal Processing
Tianying Wang, Department of Statistics
Sparse Quadratic Classification Rules Via Linear Dimension Reduction
Zhangyang (Atlas) Wang, Department of Computer Science & Engineering
Embracing Sparsity in Deep Networks: From Algorithms to Hardware
Hardikkumar Zalavadia, Harold Vance Department of Petroleum Engineering
Parametric Model Order Reduction for Adaptive Basis Selection Using Machine Learning Techniques During Well Location Optimization

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Schedule

All workshop presentations will take place in Rudder Tower (RDER) 410. Please note that the schedule is tentative and subject to change. All times listed are local time.

Welcome
Irina Gaynanova, Department of Statistics
Structural Learning and Integrative Decomposition of Multi-View Data
Horacio Florez, Harold Vance Department of Petroleum Engineering
Uncertainty Quantification and Model-Order Reduction on Linear and Nonlinear Problems: Analysis, Approaches, and Challenges
Zhangyang (Atlas) Wang, Department of Computer Science & Engineering
Embracing Sparsity in Deep Networks: From Algorithms to Hardware
Break
Eduardo Gildin, Harold Vance Department of Petroleum Engineering
Faster Reservoir Simulation and Optimization using Control System Insights
Robert Skelton, Department of Aerospace Engineering
Modeling Dynamic Systems is Not Just About Physics: Toward the Marriage of Physics and Signal Processing
Michael King, Harold Vance Department of Petroleum Engineering
Improved Localization in Upscaling of Fluid Flow in Porous Media
Lunch Break
Hardikkumar Zalavadia, Harold Vance Department of Petroleum Engineering
Parametric Model Order Reduction for Adaptive Basis Selection Using Machine Learning Techniques During Well Location Optimization
Abhinav Narasingam, Artie McFerrin Department of Chemical Engineering
Data-Driven Identification of Interpretable Reduced-Order Models Using Sparse Regression
Doowon Choi, Department of Industrial & Systems Engineering
Correlation Trees with Application to Neural Correlates Studies
Break
Tianying Wang, Department of Statistics
Sparse Quadratic Classification Rules Via Linear Dimension Reduction
Ishan Bajaj, Artie McFerrin Department of Chemical Engineering
Computational Experience with Different Reduced Models for Derivative-Free Optimization
Mahmood Ettehad, Department of Mathematics
Graph Reconstruction from Path Correlation Data
Krishna Nunna, Harold Vance Department of Petroleum Engineering
Multiscale Reservoir Simulation Using Pressure Transient Concepts
Chiranjivi Botre, Artie McFerrin Department of Chemical Engineering
Modified Nonlinear Partial Least Square (PLS) as a Model Reduction Technique with Applications to Fault Detection
Yalchin Efendiev, Department of Mathematics
Nonlocal Multicontinua Upscaling for Flows in Porous Media
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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