Kevin T. Carlberg

16 papers A* 3Journal 13
YearRankTypeTitle / Venue / Authors
2024 A* conf
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need.
ICML
Shangda Yang, Vitaly Zankin, Maximilian Balandat, Stefan Scherer, Kevin T. Carlberg, Neil Walton, Kody J. H. Law
2023 A* conf
CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations.
ICLR
Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho, Yue Chang, G. A. Pershing, Henrique Teles Maia, Maurizio M. Chiaramonte, Kevin T. Carlberg, Eitan Grinspun
2022 J jnl
Projection-tree reduced-order modeling for fast
J. Comput. Phys.
Steven N. Rodriguez, Athanasios P. Iliopoulos, Kevin T. Carlberg, Steven L. Brunton, John C. Steuben, John G. Michopoulos
2021 A* conf
Deep Conservation: A Latent-Dynamics Model for Exact Satisfaction of Physical Conservation Laws.
AAAI
Kookjin Lee, Kevin T. Carlberg
2021 J jnl
Projection-tree reduced order modeling for fast N-body computations.
CoRR
Steven N. Rodriguez, Athanasios P. Iliopoulos, Kevin T. Carlberg, Steven L. Brunton, John C. Steuben, John G. Michopoulos
2021 J jnl
Windowed least-squares model reduction for dynamical systems.
J. Comput. Phys.
Eric J. Parish, Kevin T. Carlberg
2020 J jnl
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders.
J. Comput. Phys.
Kookjin Lee, Kevin T. Carlberg
2020 J jnl
Preserving general physical properties in model reduction of dynamical systems via constrained-optimization projection.
CoRR
Alexander Schein, Kevin T. Carlberg, Matthew J. Zahr
2020 J jnl
Pressio: Enabling projection-based model reduction for large-scale nonlinear dynamical systems.
CoRR
Francesco Rizzi, Patrick J. Blonigan, Kevin T. Carlberg
2019 J jnl
An Efficient, Globally Convergent Method for Optimization Under Uncertainty Using Adaptive Model Reduction and Sparse Grids.
SIAM/ASA J. Uncertain. Quantification
Matthew J. Zahr, Kevin T. Carlberg, Drew P. Kouri
2019 J jnl
Online adaptive basis refinement and compression for reduced-order models.
CoRR
Philip Etter, Kevin T. Carlberg
2019 J jnl
Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning.
J. Comput. Phys.
Kevin T. Carlberg, Antony Jameson, Mykel J. Kochenderfer, Jeremy Morton, Liqian Peng, Freddie D. Witherden
2019 J jnl
Time-series machine-learning error models for approximate solutions to parameterized dynamical systems.
CoRR
Eric J. Parish, Kevin T. Carlberg
2019 J jnl
Windowed least-squares model reduction for dynamical systems.
CoRR
Eric J. Parish, Kevin T. Carlberg
2018 J jnl
Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations.
CoRR
Brian A. Freno, Kevin T. Carlberg
2018 J jnl
Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning.
CoRR
Kevin T. Carlberg, Antony Jameson, Mykel J. Kochenderfer, Jeremy Morton, Liqian Peng, Freddie D. Witherden