Kookjin Lee

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11 Conference contribution
11 Article
2 Conference article

Climate modeling with neural advection–diffusion equation
Choi, H., Choi, J., Hwang, J., Lee, K., Lee, D. & Park, N., Jun 2023, In: Knowledge and Information Systems. 65, 6, p. 2403-2427 25 p.
Research output: Contribution to journal › Article › peer-review
1 Scopus citations
Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models
Chowdhary, K., Hoang, C., Lee, K., Ray, J., Weirs, V. G. & Carnes, B., Nov 1 2022, In: Computer Methods in Applied Mechanics and Engineering. 401, 115396.
Research output: Contribution to journal › Article › peer-review
1 Scopus citations
Deep Sequence Models for Packet Stream Analysis and Early Decisions
Kim, M., Lee, D., Lee, K., Kim, D., Lee, S. & Kim, J., 2022, Proceedings of the 47th IEEE Conference on Local Computer Networks, LCN 2022. Oteafy, S., Bulut, E. & Tschorsch, F. (eds.). IEEE Computer Society, p. 56-63 8 p. (Proceedings - Conference on Local Computer Networks, LCN).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Enhanced alternating energy minimization methods for stochastic galerkin matrix equations
Lee, K., Elman, H. C., Powell, C. E. & Lee, D., 2022, (Accepted/In press) In: BIT Numerical Mathematics.
Research output: Contribution to journal › Article › peer-review
On surrogate learning for linear stability assessment of Navier-Stokes Eeuations with stochastic viscosity
Sousedík, B., Elman, H. C., Lee, K. & Price, R., Dec 2022, In: Applications of Mathematics. 67, 6, p. 727-749 23 p.
Research output: Contribution to journal › Article › peer-review
1 Scopus citations

3 Active

EAGER: Combatting disinformation and racial bias: A deep-learning-assisted investigation of temporal dynamics of disinformation
Lee, K. & Kwon, K. H.
National Science Foundation (NSF)
6/1/22 → 5/31/24
Project: Research project
Subcontract: SANDIA beyond fingerprinting grand challenge LDRD
Lee, K.
Sandia National Laboratories (SNL)
3/1/22 → 9/19/24
Project: Research project
Development of a graduate course on deep learning
Li, B., Yang, Y. & Lee, K.
INDUSTRY: Domestic Company
12/27/21 → 12/26/23
Project: Research project

Supplementary material from "Parametrized neural ordinary differential equations: applications to computational physics problems"
Lee, K. (Creator) & Parish, E. J. (Creator), The Royal Society, 2021
DOI: 10.6084/m9.figshare.c.5599853.v1, https://rs.figshare.com/collections/Supplementary_material_from_Parametrized_neural_ordinary_differential_equations_applications_to_computational_physics_problems_/5599853/1
Dataset
Figure S4: from Parameterized neural ordinary differential equations: applications to computational physics problems
Lee, K. (Creator) & Parish, E. J. (Creator), The Royal Society, 2021
DOI: 10.6084/m9.figshare.16627263.v2, https://rs.figshare.com/articles/journal_contribution/Figure_S4_from_Parametrized_neural_ordinary_differential_equations_applications_to_computational_physics_problems/16627263/2
Dataset
Code from Parametrized neural ordinary differential equations: applications to computational physics problems
Lee, K. (Creator) & Parish, E. J. (Creator), The Royal Society, 2021
DOI: 10.6084/m9.figshare.16557722, https://rs.figshare.com/articles/dataset/Code_from_Parametrized_neural_ordinary_differential_equations_applications_to_computational_physics_problems/16557722
Dataset
Supplementary material from "Parametrized neural ordinary differential equations: applications to computational physics problems"
Lee, K. (Creator) & Parish, E. J. (Creator), The Royal Society, 2021
DOI: 10.6084/m9.figshare.c.5599853.v2, https://rs.figshare.com/collections/Supplementary_material_from_Parametrized_neural_ordinary_differential_equations_applications_to_computational_physics_problems_/5599853/2
Dataset
Supplementary material from "Parameterized neural ordinary differential equations: applications to computational physics problems"
Lee, K. (Creator) & Parish, E. J. (Creator), The Royal Society, 2021
DOI: 10.6084/m9.figshare.c.5599853.v3, https://rs.figshare.com/collections/Supplementary_material_from_Parametrized_neural_ordinary_differential_equations_applications_to_computational_physics_problems_/5599853/3
Dataset

Kookjin Lee

Fingerprint

Collaborations and top research areas from the last five years

Dive into details

Climate modeling with neural advection–diffusion equation

Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models

Deep Sequence Models for Packet Stream Analysis and Early Decisions

Enhanced alternating energy minimization methods for stochastic galerkin matrix equations

On surrogate learning for linear stability assessment of Navier-Stokes Eeuations with stochastic viscosity

EAGER: Combatting disinformation and racial bias: A deep-learning-assisted investigation of temporal dynamics of disinformation

Subcontract: SANDIA beyond fingerprinting grand challenge LDRD

Development of a graduate course on deep learning

Supplementary material from "Parametrized neural ordinary differential equations: applications to computational physics problems"

Figure S4: from Parameterized neural ordinary differential equations: applications to computational physics problems

Code from Parametrized neural ordinary differential equations: applications to computational physics problems

Supplementary material from "Parametrized neural ordinary differential equations: applications to computational physics problems"

Supplementary material from "Parameterized neural ordinary differential equations: applications to computational physics problems"

Kookjin Lee

Fingerprint

Collaborations and top research areas from the last five years

Research output

Projects

Datasets