Kevin T. Carlberg
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Kevin Carlberg
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Nonlinear reduced-order modeling: Using machine learning to enable extreme-scale simulation for many-query problems
The network uncertainty quantification method for propagating uncertainties in component-based systems
Nonlinear model reduction: Using machine learning to enable rapid simulation of extreme-scale physics models
Time-series machine-learning error models for approximate solutions to parameterized dynamical systems
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
An efficient, globally convergent method for optimization under uncertainty using adaptive model reduction and sparse grids
Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning
Breaking Kolmogorov-width barriers in model reduction using deep convolutional autoencoders
Data-driven time parallelism via forecasting
Nonlinear model reduction: Using machine learning to enable rapid simulation of extreme-scale physics models
Nonlinear reduced-order modeling: Using machine learning to enable extreme-scale simulation for many-query problems
Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
Online adaptive basis refinement and compression for reduced-order models via vector-space sieving
Nonlinear model reduction: Using machine learning to enable extreme-scale simulation for time-critical aerospace applications
Nonlinear model reduction: Using machine learning to enable extreme-scale simulation for time-critical aerospace applications
Nonlinear model reduction: Using machine learning to enable extreme-scale simulation for many-query problems
Statistical closure modeling for reduced-order models of stationary systems by the ROMES method
Space–time least-squares Petrov–Galerkin projection for nonlinear model reduction
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
Conservative model reduction for finite-volume models
Advances in nonlinear model reduction: least-squares Petrov--Galerkin projection and machine-learning error models
Conservative model reduction for finite-volume models in CFD
Stochastic least-squares Petrov–Galerkin method for parameterized linear systems
Krylov-subspace recycling via the POD-augmented conjugate-gradient method
Accelerated solution of discrete ordinates approximation to the Boltzmann transport equation via model reduction
Error modeling for surrogates of dynamical systems using machine learning
Structure-preserving model reduction for marginally stable LTI systems
Galerkin v. least-squares Petrov--Galerkin projection in nonlinear model reduction
Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting
Adaptive $h$-refinement for reduced-order models
Preserving Lagrangian structure in nonlinear model reduction with application to structural dynamics
The ROMES method for statistical modeling of reduced-order-model error
The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows
Efficient structure-preserving model reduction for nonlinear mechanical systems with application to structural dynamics
The GNAT nonlinear model reduction method and its application to fluid dynamics problems
A low-cost, goal-oriented ‘compact proper orthogonal decomposition’ basis for model reduction of static systems
Efficient non-linear model reduction via a least-squares Petrov–Galerkin projection and compressive tensor approximations
Reduced order modeling applied to the discrete ordinates method for radiation heat transfer in participating media
Gappy data reconstruction and applications in archaeology
A method for interpolating on manifolds structural dynamics reduced-order models
An adaptive POD-Krylov reduced-order model for structural optimization
A low-cost, goal-oriented ‘compact proper orthogonal decomposition’ basis for model reduction of static systems
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