Articles with public access mandates - Kookjin LeeLearn more
Available somewhere: 20
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
K Lee, KT Carlberg
Journal of Computational Physics 404, 108973, 2020
Mandates: US Department of Energy
DPM: A novel training method for physics-informed neural networks in extrapolation
J Kim, K Lee, D Lee, SY Jhin, N Park
Proceedings of the AAAI Conference on Artificial Intelligence 35 (9), 8146-8154, 2021
Mandates: US Department of Energy
Deep Conservation: A latent-dynamics model for exact satisfaction of physical conservation laws
K Lee, K Carlberg
Proceedings of the AAAI Conference on Artificial Intelligence 35 (1), 277-285, 2021
Mandates: US Department of Energy
Parameterized neural ordinary differential equations: Applications to computational physics problems
K Lee, EJ Parish
Proceedings of the Royal Society A, 2021
Mandates: US Department of Energy
Machine learning structure preserving brackets for forecasting irreversible processes
K Lee, N Trask, P Stinis
Advances in Neural Information Processing Systems 34, 5696-5707, 2021
Mandates: US Department of Energy
A preconditioned low-rank projection method with a rank-reduction scheme for stochastic partial differential equations
K Lee, HC Elman
SIAM Journal on Scientific Computing 39 (5), S828-S850, 2017
Mandates: US National Science Foundation, US Department of Energy
Structure-preserving sparse identification of nonlinear dynamics for data-driven modeling
K Lee, N Trask, P Stinis
Mathematical and Scientific Machine Learning, 65-80, 2022
Mandates: US Department of Energy
Projection-based model reduction of dynamical systems using space–time subspace and machine learning
C Hoang, K Chowdhary, K Lee, J Ray
Computer Methods in Applied Mechanics and Engineering 389, 114341, 2022
Mandates: US Department of Energy
A Low-rank solver for the Navier--Stokes equations with uncertain viscosity
K Lee, HC Elman, B Sousedik
SIAM/ASA Journal on Uncertainty Quantification 7 (4), 1275-1300, 2019
Mandates: US National Science Foundation, US Department of Energy
Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models
K Chowdhary, C Hoang, K Lee, J Ray, VG Weirs, B Carnes
Computer Methods in Applied Mechanics and Engineering 401, 115396, 2022
Mandates: US Department of Energy
The predictive skill of convolutional neural networks models for disease forecasting
K Lee, J Ray, C Safta
Plos one 16 (7), e0254319, 2021
Mandates: US Department of Energy
Stochastic least-squares Petrov--Galerkin method for parameterized linear systems
K Lee, K Carlberg, HC Elman
SIAM/ASA Journal on Uncertainty Quantification 6 (1), 374-396, 2018
Mandates: US National Science Foundation, US Department of Energy
Inexact methods for symmetric stochastic eigenvalue problems
K Lee, B Sousedik
SIAM/ASA Journal on Uncertainty Quantification 6 (4), 1744-1776, 2018
Mandates: US National Science Foundation, US Department of Energy
On surrogate learning for linear stability assessment of Navier-Stokes equations with stochastic viscosity
B Sousedík, HC Elman, K Lee, R Price
Applications of Mathematics 67 (6), 727-749, 2022
Mandates: US National Science Foundation, US Department of Energy
Stochastic Galerkin methods for linear stability analysis of systems with parametric uncertainty
B Sousedík, K Lee
SIAM/ASA Journal on Uncertainty Quantification 10 (3), 1101-1129, 2022
Mandates: US National Science Foundation
Enhanced alternating energy minimization methods for stochastic galerkin matrix equations
K Lee, HC Elman, CE Powell, D Lee
BIT Numerical Mathematics 62 (3), 965-994, 2022
Mandates: US National Science Foundation, US Department of Energy
Predictive Skill of Deep Learning Models Trained on Limited Sequence Data.
K Lee, J Ray, C Safta
Sandia National Lab.(SNL-CA), Livermore, CA (United States), 2020
Mandates: US Department of Energy
Two Problems in Knowledge Graph Embedding: Non-Exclusive Relation Categories and Zero Gradients
N Nur, N Park, K Lee, H Kang, S Kwon
2019 IEEE International Conference on Big Data (Big Data), 1181-1186, 2019
Mandates: US Department of Energy
Parametric space? time model reduction with deep bases.
E Parish, Y Shimizu, K Lee
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States); Sandia …, 2021
Mandates: US Department of Energy
An Adaptive Basis Perspective to Improve Initialization and Accelerate Training of DNNs.
EC Cyr, M Gulian, K Lee, RG Patel, M Perego, NA Trask
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021
Mandates: US Department of Energy
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