Kevin Carlberg
Orcid: 0000-0001-8313-7720Affiliations:
- Sandia National Laboratories, Livermore, CA, USA
According to our database1,
Kevin Carlberg
authored at least 39 papers
between 2013 and 2024.
Collaborative distances:
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Bibliography
2024
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need.
CoRR, 2024
2023
Model reduction for the material point method via an implicit neural representation of the deformation map.
J. Comput. Phys., April, 2023
Proceedings of the SIGGRAPH Asia 2023 Conference Papers, 2023
Proceedings of the SIGGRAPH Asia 2023 Conference Papers, 2023
CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
2022
J. Comput. Phys., 2022
CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations.
CoRR, 2022
2021
Model reduction for the material point method via learning the deformation map and its spatial-temporal gradients.
CoRR, 2021
Model Reduction for the Material Point Method on Nonlinear Manifolds Using Deep Learning.
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021
Deep Conservation: A Latent-Dynamics Model for Exact Satisfaction of Physical Conservation Laws.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
2020
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders.
J. Comput. Phys., 2020
Preserving general physical properties in model reduction of dynamical systems via constrained-optimization projection.
CoRR, 2020
Domain-decomposition least-squares Petrov-Galerkin (DD-LSPG) nonlinear model reduction.
CoRR, 2020
Pressio: Enabling projection-based model reduction for large-scale nonlinear dynamical systems.
CoRR, 2020
2019
SIAM J. Sci. Comput., 2019
An Efficient, Globally Convergent Method for Optimization Under Uncertainty Using Adaptive Model Reduction and Sparse Grids.
SIAM/ASA J. Uncertain. Quantification, 2019
Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning.
J. Comput. Phys., 2019
The network uncertainty quantification method for propagating uncertainties in component-based systems.
CoRR, 2019
Time-series machine-learning error models for approximate solutions to parameterized dynamical systems.
CoRR, 2019
CoRR, 2019
Statistical closure modeling for reduced-order models of stationary systems by the ROMES method.
CoRR, 2019
2018
SIAM/ASA J. Uncertain. Quantification, 2018
Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations.
CoRR, 2018
2017
J. Comput. Phys., 2017
CoRR, 2017
2016
SIAM J. Matrix Anal. Appl., 2016
2015
Preserving Lagrangian Structure in Nonlinear Model Reduction with Application to Structural Dynamics.
SIAM J. Sci. Comput., 2015
SIAM/ASA J. Uncertain. Quantification, 2015
2014
2013
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., 2013
The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows.
J. Comput. Phys., 2013