Reduced Order Modeling for systems with component nonlinearities.
Abstract not provided.
Konstantinos, Vlachas↗
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Results for “Reduce order modeling”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Least-Squares Petrov?Galerkin Reduced-Order Models for Steady Hypersonic Aerodynamics.
Abstract not provided.
Blonigan, Patrick↗
Preconditioned Least-Squares Petrov-Galerkin Reduced Order Models for Fluid and Solid Mechanics Problems.
Abstract not provided.
Lindsay, Payton↗
A MACHINE LEARNING FRAMEWORK FOR ALLEVIATING BOTTLENECKS OF PROJECTION-BASED REDUCED ORDER MODELS.
Abstract not provided.
Konstantinos, Vlachas↗
Projection-based reduced order models for large-scale multiphysics applications.
Abstract not provided.
Tencer, John↗
Earthquake induced damage estimation in structural systems using parametric physics-based Reduced-Order Models (ROMs).
Abstract not provided.
Konstantinos, Vlachas↗
E3SM Atmosphere surrogate construction using machine learning and reduced order modeling methods.
Abstract not provided.
Chowdhary, Kamaljit↗
Using Reduced Order Models to Predict Modal Coupling in Jointed Structures.
Abstract not provided.
Singh, Aabhas↗
NRAP-Open-IAM Multisegmented Wellbore Reduced-Order Model: Improvement and Quality Assurance
The multisegmented wellbore model (MSW) semi-analytically estimates the amount of CO 2 and brine leakage from a leaking legacy well by segmenting it into intervals to simulate site-specific stratigraphic and hydrogeologic properties. The model is a component of the National Risk Assessment Partnership Open-Source Integrated Assessment Model (NRAP-Open-IAM), which was developed to perform risk assessment for geologic CO 2 storage. The new wellbore leakage model, which uses deep learning networks for a caprock segment, was developed to enhance the analytical MSW. The model was trained and validated using a synthetic data set of Subsurface Transport Over Multiple Phases (STOMP) multiphase flow simulations from various geological, well attribute, and operational conditions to ensure its quality. The results demonstrate that the model is more accurate than the existing model in predicting the transport of two-phase fluids (brine and injected CO 2 ) through the well. This report provides a detailed explanation of the model development and quality assurance.
58 GEOSCIENCES↗
Grid Tailored Reduced-Order Models for Steady Hypersonic Aerodynamics .
Abstract not provided.
Ching, David↗
A Physics-based Reduced Order Model with Machine Learning Boosted Hyper-Reduction.
Abstract not provided.
Konstantinos, Vlachas↗
A Lagrange Multiplier Partitioned Scheme for Coupling Reduced Order Models.
Abstract not provided.
de Castro, Amy↗
E3SM Atmosphere surrogate construction and calibration using machine learning and reduced order modeling .
Abstract not provided.
Wagman, Benjamin↗
Grid Tailored Reduced-Order Models for Steady Hypersonic Aerodynamics .
Abstract not provided.
Blonigan, Patrick↗
Variational AutoEncoder (VAE) Boosted Parametric Reduced Order Modelling (pROM).
Abstract not provided.
Vlachas, Konstantinos↗
Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds.
Abstract not provided.
Kadeethum, Teeratorn↗
Reduced order modeling with boosting Barlow Twins self supervised learning for contact problem in a compressible hyperelastic material.
Abstract not provided.
Kadeethum, Teeratorn↗