Data-Driven Model Reduction, Scientific Frontiers, and Applications

May 1, 2018
Texas A&M University
College Station, TX
Rudder Tower (RDER) 410

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/.

Organizing Committee

Yalchin Efendiev, Institute for Scientific Computation
Eduardo Gildin, Energi Simulation
Joseph Kwon, Artie McFerrin Department of Chemical Engineering

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

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.

Tuesday, May 1, 2018

8:25–8:30 a.m.: Welcome
8:30–9 a.m.: Irina Gaynanova, Department of Statistics; Structural Learning and Integrative Decomposition of Multi-View Data
9–9:30 a.m.: Horacio Florez, Harold Vance Department of Petroleum Engineering; Uncertainty Quantification and Model-Order Reduction on Linear and Nonlinear Problems: Analysis, Approaches, and Challenges
9:30–10 a.m.: Zhangyang (Atlas) Wang, Department of Computer Science & Engineering; Embracing Sparsity in Deep Networks: From Algorithms to Hardware
10–10:30 a.m.: Break
10:30–11 a.m.: Eduardo Gildin, Harold Vance Department of Petroleum Engineering; Faster Reservoir Simulation and Optimization using Control System Insights
11–11:30 a.m.: Robert Skelton, Department of Aerospace Engineering; Modeling Dynamic Systems is Not Just About Physics: Toward the Marriage of Physics and Signal Processing
11:30 a.m.–noon: Michael King, Harold Vance Department of Petroleum Engineering; Improved Localization in Upscaling of Fluid Flow in Porous Media
noon–1:15 p.m.: Lunch Break
1:15–1:35 p.m.: Hardikkumar Zalavadia, Harold Vance Department of Petroleum Engineering; Parametric Model Order Reduction for Adaptive Basis Selection Using Machine Learning Techniques During Well Location Optimization
1:35–1:55 p.m.: Abhinav Narasingam, Artie McFerrin Department of Chemical Engineering; Data-Driven Identification of Interpretable Reduced-Order Models Using Sparse Regression
1:55–2:15 p.m.: Doowon Choi, Department of Industrial & Systems Engineering; Correlation Trees with Application to Neural Correlates Studies
2:15–2:45 p.m.: Break
2:45–3:05 p.m.: Tianying Wang, Department of Statistics; Sparse Quadratic Classification Rules Via Linear Dimension Reduction
3:05–3:25 p.m.: Ishan Bajaj, Artie McFerrin Department of Chemical Engineering; Computational Experience with Different Reduced Models for Derivative-Free Optimization
3:25–3:45 p.m.: Mahmood Ettehad, Department of Mathematics; Graph Reconstruction from Path Correlation Data
3:45–4:05 p.m.: Krishna Nunna, Harold Vance Department of Petroleum Engineering; Multiscale Reservoir Simulation Using Pressure Transient Concepts
4:05–4:25 p.m.: Chiranjivi Botre, Artie McFerrin Department of Chemical Engineering; Modified Nonlinear Partial Least Square (PLS) as a Model Reduction Technique with Applications to Fault Detection
4:25–4:45 p.m.: Yalchin Efendiev, Department of Mathematics; Nonlocal Multicontinua Upscaling for Flows in Porous Media
4:45–4:50 p.m.: Closing Remarks

Contact Information

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

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