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- The Institute for Scientific Computation at Texas A&M University
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Events
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Fall 2018
- Speakers
- Which ReLU Net Architectures Give Rise to Exploding and Vanishing Gradients?
- Development of Proxy Models for Reservoir Simulation by Sparsity Promoting Methods and Machine Learning Techniques
- Learning the Right Tolls - Traffic Flow Optimization Through Micro-Tolling
- Data-Driven Identification of Interpretable Reduced-Order Models Using Sparse Regression
- Deep Learning in Detecting Illicit Nuclear Materials
- Optimal Hyperparameter Estimation in Support Vector Machine
- Deep Learning: Methods and Applications
- Pattern Recognition for Small-Sample Applications
- Deep Multiscale Model Learning
- Interpretable Deep Learning for Drug Discovery
- Deep Learning in Construction Science: Automated Contextual Information Analysis for Resource Allocation
- A (Fairly Rough) Tour of Our Recent Works in Computer Vision, Machine Learning, and Their Applications
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Fall 2018
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Fall 2019
- Speakers
- Koopman Operator Based Model Predictive Control for Hydraulic Fracturing
- Applications of Machine Learning to Life-Cycle Reservoir Engineering: From Drilling to Reservoir Simulation
- Learning an Interpretable Control Policy Through Deep Neural Networks
- Deep Learning in Detecting Illicit Nuclear Materials
- Applications of Machine Learning to Predict the Physical Properties of the Subsurface
- A Decoupling Principle in Stochastic Optimal Control and Its Implications
- Neural Network Prediction of Dengue Fever Severity Based on Genetic Polymorphisms
- Reduced-Order Deep Learning for Flow Dynamics. The Interplay Between Deep Learning and Model Reduction.
- Re-Comparing ImageNet Classifiers: Accuracy Should Not Be the Only Goal
- Speakers
- Model Reduction for Simplifying Thermal Efficiency Planning at City Scale
- Tangent Subspace Descent on Quotient Manifolds
- Model Reduction for Reservoir Simulation at the Crossroads: Is it Feasible to Construct an Input-Output Invariant Proxy Model?
- Statistical Modeling of Random Shifting and Shapes in Functional Data and Uncertainty Quantification via Landmark-Embedded Hierarchical Gaussian Processes
- Transformer-based Hybrid Modeling and Control of Evolving, Nonlinear Processes
- Homogenization of Layered Heterostructures
- Two Cases of Leveraging Solution Character to Expedite Computation
- Scalable algorithms for Gaussian Process Regression via Kernel Packets
- A Reduced Order Iterative Linear Quadratic Regulator (ILQR) Technique for the Optimal Control of Nonlinear Partial Differential Equations
- Multicontinuum Homogenization as a Model Reduction Technique
- Neural Operator-Based Rapid Forecast of CO2 Pressure and Saturation Distribution During Geological Carbon Storage
- Speakers
- Convergence of Coordinate Ascent Variational Inference
- Uncertainty Quantification with Hyper-Reduced Order Models
- Information State Based Reinforcement Learning for the Control of Partially Observed Nonlinear Systems
- Nonintrusive Reduced-Order Modeling for Reservoir Simulation Using Operator Inference
- Tangent Subspace Descent via Discontinuous Subspace Selections on Fixed-Rank Manifolds
- Creating Universal Chemical Language
- Modeling and Optimization of Optical Layered Heterostructures
- New Applications of the Theory of Functional Connections
- Convergence and Error Control of Consistent PINNs for Elliptic PDEs
- Effective Diffusion Matrices via Fokker—Planck—Kolmogorov Equations and Beyond
- Online Compression of High-Order CFD Solutions Using Machine Learning
- Amortizing the Costs of Scientific Machine Learning at Scale: Timely Challenges and Opportunities
- Speakers
- Reduced Order Model-Based Process Synthesis, Optimization, and Intensification
- Reexamining the Proton-Radius Problem Using Constrained Gaussian Processes
- Model Reduction and Decision Making Under Uncertainty for Optimal Management of Infrastructures
- Model Reduction of Coupled Flow and Geomechanics: Ideas from Structural Mechanics
- An Operator Theoretic Framework for Data-Driven Identification and Control of a Hydraulic Fracturing Process
- Optimizing Gas Injection EOR in Unconventional Reservoirs Using the Fast Marching Method
- Risk Analysis of Rare Events Through Bi-Directionality in Fault and Event Trees
- Model Order Reduction for Radiation Transport
- Uncertainty Quantification with Gaussian Processes: Uniform Error Bounds and Convergence Properties
- Data-dependent Kernel Approximation for Better Generalization
- Randomized Model Reduction for Large Scale Systems
- Partially-Observed Boolean Dynamical Systems
- Upscaling of Multi-Phase Flow and Transport Using Non-Local Multi-Continuum Approach
- Parametric Uncertainty Quantification Using Proper Generalized Decomposition Applied to Neutron Diffusion