Kevin T. Carlberg

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 15

Top Topics

Kalman Filtering

Dynamical Systems

Nonlinear Equations

Top Venues

J. Comput. Phys.

SIAM/ASA J. Uncertain. Quantification

Publications

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)
Steven N. Rodriguez, Athanasios P. Iliopoulos, Kevin T. Carlberg, Steven L. Brunton, John C. Steuben, John G. Michopoulos
-body computations. J. Comput. Phys. 459 (2022)
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 (2021)
Kookjin Lee, Kevin T. Carlberg
Deep Conservation: A Latent-Dynamics Model for Exact Satisfaction of Physical Conservation Laws. AAAI (2021)
Eric J. Parish, Kevin T. Carlberg
Windowed least-squares model reduction for dynamical systems. J. Comput. Phys. 426 (2021)
Kookjin Lee, Kevin T. Carlberg
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders. J. Comput. Phys. 404 (2020)
Francesco Rizzi, Patrick J. Blonigan, Kevin T. Carlberg
Pressio: Enabling projection-based model reduction for large-scale nonlinear dynamical systems. CoRR (2020)
A. Schein, Kevin T. Carlberg, Matthew J. Zahr
Preserving general physical properties in model reduction of dynamical systems via constrained-optimization projection. CoRR (2020)
Philip Etter, Kevin T. Carlberg
Online adaptive basis refinement and compression for reduced-order models. CoRR (2019)
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 (2019)
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) (2019)
Eric J. Parish, Kevin T. Carlberg
Time-series machine-learning error models for approximate solutions to parameterized dynamical systems. CoRR (2019)
Eric J. Parish, Kevin T. Carlberg
Windowed least-squares model reduction for dynamical systems. CoRR (2019)
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 (2018)
Brian A. Freno, Kevin T. Carlberg
Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations. CoRR (2018)