Data-Driven Model Reduction, Scientific Frontiers, and Applications
- 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/.
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Organizing Committee
- Yalchin Efendiev, Institute for Scientific Computation
- Eduardo Gildin, Energi Simulation
- Joseph Kwon, Artie McFerrin Department of Chemical Engineering
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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.