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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/Spring2019/.

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Organizing Committee

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Speakers

Akhil Arora, Artie McFerrin Department of Chemical Engineering
Reduced Order Model-Based Process Synthesis, Optimization, and Intensification
Patrick Behne, Department of Nuclear Engineering
Model Order Reduction for \(S_n\) Radiation Transport
Anirban Bhattacharya, Department of Statistics
Reexamining the Proton-Radius Problem Using Constrained Gaussian Processes
Ulisses Braga-Neto, Department of Electrical & Computer Engineering
Partially-Observed Boolean Dynamical Systems
Suman Chakravorty, Department of Aerospace Engineering
Randomized Model Reduction for Large Scale Systems
Yalchin Efendiev, Department of Mathematics
Upscaling of Multi-Phase Flow and Transport Using Non-Local Multi-Continuum Approach
Eduardo Gildin, Harold Vance Department of Petroleum Engineering
Model Reduction of Coupled Flow and Geomechanics: Ideas from Structural Mechanics
Mike King, Harold Vance Department of Petroleum Engineering
Optimizing Gas Injection EOR in Unconventional Reservoirs Using the Fast Marching Method
Pallavi Kumari, Artie McFerrin Department of Chemical Engineering
Risk Analysis of Rare Events Through Bi-Directionality in Fault and Event Trees
Joseph Kwon, Artie McFerrin Department of Chemical Engineering
An Operator Theoretic Framework for Data-Driven Identification and Control of a Hydraulic Fracturing Process
Arash Noshadravan, Zachry Department of Civil Engineering
Model Reduction and Decision Making Under Uncertainty for Optimal Management of Infrastructures
Zachary Prince, Department of Nuclear Engineering
Parametric Uncertainty Quantification Using Proper Generalized Decomposition Applied to Neutron Diffusion
Shahin Shahrampour, Department of Industrial & Systems Engineering
Data-dependent Kernel Approximation for Better Generalization
Rui Tuo, Department of Industrial & Systems Engineering
Uncertainty Quantification with Gaussian Processes: Uniform Error Bounds and Convergence Properties

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Schedule

All workshop presentations will take place in Joe C. Richardson Petroleum Engineering Building (RICH) 910. Please note that the schedule is tentative and subject to change. All times listed are local time.

Welcome
Patrick Behne, Department of Nuclear Engineering
Model Order Reduction for \(S_n\) Radiation Transport
Akhil Arora, Artie McFerrin Department of Chemical Engineering
Reduced Order Model-Based Process Synthesis, Optimization, and Intensification
Ulisses Braga-Neto, Department of Electrical & Computer Engineering
Partially-Observed Boolean Dynamical Systems
Break
Eduardo Gildin, Harold Vance Department of Petroleum Engineering
Model Reduction of Coupled Flow and Geomechanics: Ideas from Structural Mechanics
Zachary Prince, Department of Nuclear Engineering
Parametric Uncertainty Quantification Using Proper Generalized Decomposition Applied to Neutron Diffusion
Shahin Shahrampour, Department of Industrial & Systems Engineering
Data-dependent Kernel Approximation for Better Generalization
Lunch
Mike King, Harold Vance Department of Petroleum Engineering
Optimizing Gas Injection EOR in Unconventional Reservoirs Using the Fast Marching Method
Arash Noshadravan, Zachry Department of Civil Engineering
Model Reduction and Decision Making Under Uncertainty for Optimal Management of Infrastructures
Suman Chakravorty, Department of Aerospace Engineering
Randomized Model Reduction for Large Scale Systems
Pallavi Kumari, Artie McFerrin Department of Chemical Engineering
Risk Analysis of Rare Events Through Bi-Directionality in Fault and Event Trees
Break
Joseph Kwon, Artie McFerrin Department of Chemical Engineering
An Operator Theoretic Framework for Data-Driven Identification and Control of a Hydraulic Fracturing Process
Anirban Bhattacharya, Department of Statistics
Reexamining the Proton-Radius Problem Using Constrained Gaussian Processes
Rui Tuo, Department of Industrial & Systems Engineering
Uncertainty Quantification with Gaussian Processes: Uniform Error Bounds and Convergence Properties
Yalchin Efendiev, Department of Mathematics
Upscaling of Multi-Phase Flow and Transport Using Non-Local Multi-Continuum Approach
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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