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ORCIDKevin Carlberg
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- affiliation: Sandia National Laboratories, Livermore, CA, USA
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2020 – today
- 2024
[c6]Shangda Yang, Vitaly Zankin, Maximilian Balandat, Stefan Scherer, Kevin T. Carlberg, Neil Walton, Kody J. H. Law:
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need. ICML 2024- [i28]Shangda Yang, Vitaly Zankin, Maximilian Balandat, Stefan Scherer, Kevin Carlberg, Neil Walton, Kody J. H. Law:
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need. CoRR abs/2402.02111 (2024) - 2023
- [j16]Peter Yichen Chen
, Maurizio M. Chiaramonte, Eitan Grinspun, Kevin Carlberg:
Model reduction for the material point method via an implicit neural representation of the deformation map. J. Comput. Phys. 478: 111908 (2023) - [c5]Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho, Yue Chang, G. A. Pershing, Henrique Teles Maia, Maurizio M. Chiaramonte, Kevin T. Carlberg, Eitan Grinspun:
CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations. ICLR 2023 - [c4]Zeshun Zong, Xuan Li, Minchen Li, Maurizio M. Chiaramonte, Wojciech Matusik, Eitan Grinspun, Kevin Carlberg, Chenfanfu Jiang, Peter Yichen Chen:
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture. SIGGRAPH Asia 2023: 78:1-78:11 - [c3]Yue Chang, Peter Yichen Chen, Zhecheng Wang, Maurizio M. Chiaramonte, Kevin Carlberg, Eitan Grinspun:
LiCROM: Linear-Subspace Continuous Reduced Order Modeling with Neural Fields. SIGGRAPH Asia 2023: 111:1-111:12 - [i27]Yue Chang, Peter Yichen Chen, Zhecheng Wang, Maurizio M. Chiaramonte, Kevin Carlberg, Eitan Grinspun:
LiCROM: Linear-Subspace Continuous Reduced Order Modeling with Neural Fields. CoRR abs/2310.15907 (2023) - [i26]Zeshun Zong, Xuan Li, Minchen Li, Maurizio M. Chiaramonte, Wojciech Matusik, Eitan Grinspun, Kevin Carlberg, Chenfanfu Jiang, Peter Yichen Chen:
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture. CoRR abs/2310.17790 (2023) - 2022
- [j15]Steven N. Rodriguez, Athanasios P. Iliopoulos, Kevin T. Carlberg, Steven L. Brunton, John C. Steuben, John G. Michopoulos:
Projection-tree reduced-order modeling for fast N-body computations. J. Comput. Phys. 459: 111141 (2022) - [i25]Payton Lindsay, Jeffrey Fike, Irina Tezaur, Kevin Carlberg:
Preconditioned Least-Squares Petrov-Galerkin Reduced Order Models. CoRR abs/2203.12180 (2022) - [i24]Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho, G. A. Pershing, Henrique Teles Maia, Maurizio M. Chiaramonte, Kevin Carlberg, Eitan Grinspun:
CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations. CoRR abs/2206.02607 (2022) - [i23]Meera Hahn, Kevin Carlberg, Ruta Desai, James Hillis:
Learning a Visually Grounded Memory Assistant. CoRR abs/2210.03787 (2022) - 2021
- [j14]Eric J. Parish, Kevin T. Carlberg:
Windowed least-squares model reduction for dynamical systems. J. Comput. Phys. 426: 109939 (2021) - [c2]Kookjin Lee, Kevin T. Carlberg:
Deep Conservation: A Latent-Dynamics Model for Exact Satisfaction of Physical Conservation Laws. AAAI 2021: 277-285 - [c1]Peter Yichen Chen, Maurizio M. Chiaramonte, Eitan Grinspun, Kevin Carlberg:
Model Reduction for the Material Point Method on Nonlinear Manifolds Using Deep Learning. AAAI Spring Symposium: MLPS 2021 - [i22]Steven N. Rodriguez, Athanasios P. Iliopoulos, Kevin T. Carlberg, Steven L. Brunton, John C. Steuben, John G. Michopoulos:
Projection-tree reduced order modeling for fast N-body computations. CoRR abs/2103.01983 (2021) - [i21]Peter Yichen Chen, Maurizio M. Chiaramonte, Eitan Grinspun, Kevin Carlberg:
Model reduction for the material point method via learning the deformation map and its spatial-temporal gradients. CoRR abs/2109.12390 (2021) - 2020
- [j13]Kookjin Lee, Kevin T. Carlberg:
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders. J. Comput. Phys. 404 (2020) - [i20]Francesco Rizzi, Patrick J. Blonigan, Kevin T. Carlberg:
Pressio: Enabling projection-based model reduction for large-scale nonlinear dynamical systems. CoRR abs/2003.07798 (2020) - [i19]Chi Hoang, Youngsoo Choi, Kevin Carlberg:
Domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) nonlinear model reduction. CoRR abs/2007.11835 (2020) - [i18]Benjamin Newman, Kevin Carlberg, Ruta Desai:
Optimal Assistance for Object-Rearrangement Tasks in Augmented Reality. CoRR abs/2010.07358 (2020) - [i17]Alexander Schein, Kevin T. Carlberg, Matthew J. Zahr:
Preserving general physical properties in model reduction of dynamical systems via constrained-optimization projection. CoRR abs/2011.13998 (2020)
2010 – 2019
- 2019
- [j12]Kevin T. Carlberg, Antony Jameson, Mykel J. Kochenderfer, Jeremy Morton, Liqian Peng, Freddie D. Witherden:
Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning. J. Comput. Phys. 395: 105-124 (2019) - [j11]Matthew J. Zahr, Kevin T. Carlberg, Drew P. Kouri:
An Efficient, Globally Convergent Method for Optimization Under Uncertainty Using Adaptive Model Reduction and Sparse Grids. SIAM/ASA J. Uncertain. Quantification 7(3): 877-912 (2019) - [j10]Youngsoo Choi, Kevin Carlberg:
Space-Time Least-Squares Petrov-Galerkin Projection for Nonlinear Model Reduction. SIAM J. Sci. Comput. 41(1): A26-A58 (2019) - [j9]Kevin Carlberg, Lukas Brencher, Bernard Haasdonk, Andrea Barth:
Data-Driven Time Parallelism via Forecasting. SIAM J. Sci. Comput. 41(3): B466-B496 (2019) - [i16]Stefano Pagani, Andrea Manzoni, Kevin Carlberg:
Statistical closure modeling for reduced-order models of stationary systems by the ROMES method. CoRR abs/1901.02792 (2019) - [i15]Philip Etter, Kevin T. Carlberg:
Online adaptive basis refinement and compression for reduced-order models. CoRR abs/1902.10659 (2019) - [i14]Eric J. Parish, Kevin T. Carlberg:
Time-series machine-learning error models for approximate solutions to parameterized dynamical systems. CoRR abs/1907.11822 (2019) - [i13]Kevin Carlberg, Sofia Guzzetti, Mohammad Khalil, Khachik Sargsyan:
The network uncertainty quantification method for propagating uncertainties in component-based systems. CoRR abs/1908.11476 (2019) - [i12]Eric J. Parish, Kevin T. Carlberg:
Windowed least-squares model reduction for dynamical systems. CoRR abs/1910.11388 (2019) - 2018
- [j8]Kevin Carlberg, Youngsoo Choi, Syuzanna Sargsyan:
Conservative model reduction for finite-volume models. J. Comput. Phys. 371: 280-314 (2018) - [j7]Kookjin Lee, Kevin Carlberg, Howard C. Elman:
Stochastic Least-Squares Petrov-Galerkin Method for Parameterized Linear Systems. SIAM/ASA J. Uncertain. Quantification 6(1): 374-396 (2018) - [i11]Brian A. Freno, Kevin T. Carlberg:
Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations. CoRR abs/1808.02097 (2018) - [i10]Kevin T. Carlberg, Antony Jameson, Mykel J. Kochenderfer, Jeremy Morton, Liqian Peng, Freddie D. Witherden:
Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning. CoRR abs/1812.01177 (2018) - [i9]Kookjin Lee, Kevin Carlberg:
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders. CoRR abs/1812.08373 (2018) - 2017
- [j6]Kevin Carlberg, Matthew B. Jones, Harbir Antil:
Galerkin v. least-squares Petrov-Galerkin projection in nonlinear model reduction. J. Comput. Phys. 330: 693-734 (2017) - [i8]Sumeet Trehan, Kevin Carlberg, Louis J. Durlofsky:
Error estimation for surrogate models of dynamical systems using machine learning. CoRR abs/1701.03240 (2017) - [i7]Youngsoo Choi, Kevin Carlberg:
Space-time least-squares Petrov-Galerkin projection for nonlinear model reduction. CoRR abs/1703.04560 (2017) - [i6]Kevin Carlberg, Youngsoo Choi, Syuzanna Sargsyan:
Conservative model reduction for finite-volume models. CoRR abs/1711.11550 (2017) - 2016
- [j5]Kevin Carlberg, Virginia Forstall, Ray Tuminaro:
Krylov-Subspace Recycling via the POD-Augmented Conjugate-Gradient Method. SIAM J. Matrix Anal. Appl. 37(3): 1304-1336 (2016) - [i5]Kevin Carlberg, Lukas Brencher, Bernard Haasdonk, Andrea Barth:
Data-driven time parallelism via forecasting. CoRR abs/1610.09049 (2016) - 2015
- [j4]Martin Drohmann, Kevin Carlberg:
The ROMES Method for Statistical Modeling of Reduced-Order-Model Error. SIAM/ASA J. Uncertain. Quantification 3(1): 116-145 (2015) - [j3]Kevin Carlberg, Ray S. Tuminaro, Paul T. Boggs:
Preserving Lagrangian Structure in Nonlinear Model Reduction with Application to Structural Dynamics. SIAM J. Sci. Comput. 37(2) (2015) - [i4]Kevin Carlberg, Matthew F. Barone, Harbir Antil:
Galerkin v. discrete-optimal projection in nonlinear model reduction. CoRR abs/1504.03749 (2015) - 2014
- [i3]Kevin Carlberg, Ray Tuminaro, Paul T. Boggs:
Preserving Lagrangian structure in nonlinear model reduction with application to structural dynamics. CoRR abs/1401.8044 (2014) - [i2]Kevin Carlberg:
Adaptive $h$-refinement for reduced-order models. CoRR abs/1404.0442 (2014) - [i1]Martin Drohmann, Kevin Carlberg:
The ROMES method for statistical modeling of reduced-order-model error. CoRR abs/1405.5170 (2014) - 2013
- [j2]Kevin Carlberg, Charbel Farhat, Julien Cortial, David Amsallem:
The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows. J. Comput. Phys. 242: 623-647 (2013) - [j1]Kevin Carlberg, Charbel Farhat, Julien Cortial, David Amsallem:
Corrigendum to "The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows" [J. Comput. Physics 242 (2013) 623-647]. J. Comput. Phys. 250: 713 (2013)
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last updated on 2024-10-07 22:16 CEST by the dblp team
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