672 citations · 1.4k across the 27 of their papers we have counts for
32 papers
Learning a Visually Grounded Memory Assistant
Meera Hahn, Kevin Carlberg, Ruta Desai +1
We introduce a novel interface for large scale collection of human memory and assistance. Using the 3D Matterport simulator we create a realistic indoor environments in which we ha…
CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations
Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho +6
The long runtime of high-fidelity partial differential equation (PDE) solvers makes them unsuitable for time-critical applications. We propose to accelerate PDE solvers using reduc…
Preconditioned Least-Squares Petrov-Galerkin Reduced Order Models
Payton Lindsay, Jeffrey Fike, Irina Tezaur +1
This paper introduces a methodology for improving the accuracy and efficiency of reduced order models (ROMs) constructed using the least-squares Petrov-Galerkin (LSPG) projection m…
Model reduction for the material point method via an implicit neural representation of the deformation map
Peter Yichen Chen, Maurizio M. Chiaramonte, Eitan Grinspun +1
This work proposes a model-reduction approach for the material point method on nonlinear manifolds. Our technique approximates the by approximating the deform…
Projection-tree reduced order modeling for fast N-body computations
Steven N. Rodriguez, Athanasios P. Iliopoulos, Kevin T. Carlberg +3
This work presents a data-driven reduced-order modeling framework to accelerate the computations of -body dynamical systems and their pair-wise interactions. The proposed framew…
Preserving general physical properties in model reduction of dynamical systems via constrained-optimization projection
A. Schein, K. T. Carlberg, M. J. Zahr
Model-reduction techniques aim to reduce the computational complexity of simulating dynamical systems by applying a (Petrov-)Galerkin projection process that enforces the dynamics…
Optimal Assistance for Object-Rearrangement Tasks in Augmented Reality
Benjamin Newman, Kevin Carlberg, Ruta Desai
Augmented-reality (AR) glasses that will have access to onboard sensors and an ability to display relevant information to the user present an opportunity to provide user assistance…
Domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) nonlinear model reduction
Chi Hoang, Youngsoo Choi, Kevin Carlberg
A novel domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) model-reduction method applicable to parameterized systems of nonlinear algebraic equations (e.g., arising from…
Pressio: Enabling projection-based model reduction for large-scale nonlinear dynamical systems
Francesco Rizzi, Patrick J. Blonigan, Eric J. Parish +1
This work introduces Pressio, an open-source project aimed at enabling leading-edge projection-based reduced order models (ROMs) for large-scale nonlinear dynamical systems in scie…
Windowed least-squares model reduction for dynamical systems
Eric J. Parish, Kevin T. Carlberg
This work proposes a windowed least-squares (WLS) approach for model-reduction of dynamical systems. The proposed approach sequentially minimizes the time-continuous full-order-mod…
Deep Conservation: A latent-dynamics model for exact satisfaction of physical conservation laws
Kookjin Lee, Kevin Carlberg
This work proposes an approach for latent-dynamics learning that exactly enforces physical conservation laws. The method comprises two steps. First, the method computes a low-dimen…
The network uncertainty quantification method for propagating uncertainties in component-based systems
Kevin Carlberg, Sofia Guzzetti, Mohammad Khalil +1
This work introduces the network uncertainty quantification (NetUQ) method for performing uncertainty propagation in systems composed of interconnected components. The method assum…
Time-series machine-learning error models for approximate solutions to parameterized dynamical systems
Eric J. Parish, Kevin T. Carlberg
This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical systems. In particular, we extend the machine-le…
Online adaptive basis refinement and compression for reduced-order models via vector-space sieving
Philip A. Etter, Kevin T. Carlberg
In many applications, projection-based reduced-order models (ROMs) have demonstrated the ability to provide rapid approximate solutions to high-fidelity full-order models (FOMs). H…
Statistical closure modeling for reduced-order models of stationary systems by the ROMES method
Stefano Pagani, Andrea Manzoni, Kevin Carlberg
This work proposes a technique for constructing a statistical closure model for reduced-order models (ROMs) applied to stationary systems modeled as parameterized systems of algebr…
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
Kookjin Lee, Kevin Carlberg
Nearly all model-reduction techniques project the governing equations onto a linear subspace of the original state space. Such subspaces are typically computed using methods such a…
Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning
Kevin T. Carlberg, Antony Jameson, Mykel J. Kochenderfer +3
Data I/O poses a significant bottleneck in large-scale CFD simulations; thus, practitioners would like to significantly reduce the number of times the solution is saved to disk, ye…
An efficient, globally convergent method for optimization under uncertainty using adaptive model reduction and sparse grids
Matthew J. Zahr, Kevin T. Carlberg, Drew P. Kouri
This work introduces a new method to efficiently solve optimization problems constrained by partial differential equations (PDEs) with uncertain coefficients. The method leverages…
Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations
Brian A. Freno, Kevin T. Carlberg
This work proposes a machine-learning framework for constructing statistical models of errors incurred by approximate solutions to parameterized systems of nonlinear equations. The…
Conservative model reduction for finite-volume models
Kevin Carlberg, Youngsoo Choi, Syuzanna Sargsyan
This work proposes a method for model reduction of finite-volume models that guarantees the resulting reduced-order model is conservative, thereby preserving the structure intrinsi…
Structure-preserving model reduction for marginally stable LTI systems
Liqian Peng, Kevin Carlberg
This work proposes a structure-preserving model reduction method for marginally stable linear time-invariant (LTI) systems. In contrast to Lyapunov-stability-based approaches---whi…
Space-time least-squares Petrov-Galerkin projection for nonlinear model reduction
Youngsoo Choi, Kevin Carlberg
This work proposes a space-time least-squares Petrov-Galerkin (ST-LSPG) projection method for model reduction of nonlinear dynamical systems. In contrast to typical nonlinear model…
Stochastic Least-Squares Petrov-Galerkin Method for Parameterized Linear Systems
Kookjin Lee, Kevin Carlberg, Howard C. Elman
We consider the numerical solution of parameterized linear systems where the system matrix, the solution, and the right-hand side are parameterized by a set of uncertain input para…
Error modeling for surrogates of dynamical systems using machine learning
Sumeet Trehan, Kevin Carlberg, Louis J. Durlofsky
A machine-learning-based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimen…