Kyongmin Yeo

Publication Activity (10 Years)

Years Active: 2010-2023
Publications (10 Years): 23

Top Topics

Diffusion Process

Dynamical Systems

Chaotic Time Series

Top Venues

J. Comput. Phys.

SIAM J. Sci. Comput.

Publications

Takuya Kurihana, Kyongmin Yeo, Daniela Szwarcman, Bruce Elmegreen, S. Karthik Mukkavilli, Johannes Schmude, Levente J. Klein
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network. CoRR (2023)
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Roman Vaculín
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series. CoRR (2023)
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Dzung Phan, Roman Vaculín, Jayant Kalagnanam
An End-to-End Time Series Model for Simultaneous Imputation and Forecast. CoRR (2023)
Xiao Liu, Kyongmin Yeo
Inverse Models for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes. Technometrics 65 (3) (2023)
Mykhaylo Zayats, Malgorzata J. Zimon, Kyongmin Yeo, Sergiy Zhuk
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks. CoRR (2022)
Kyongmin Yeo, Zan Li, Wesley M. Gifford
Generative Adversarial Network for Probabilistic Forecast of Random Dynamical Systems. SIAM J. Sci. Comput. 44 (4) (2022)
Arka Daw, Kyongmin Yeo, Anuj Karpatne, Levente J. Klein
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring. CoRR (2022)
Mykhaylo Zayats, Malgorzata J. Zimon, Kyongmin Yeo, Sergiy Zhuk
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks. CDC (2022)
Arka Daw, Kyongmin Yeo, Anuj Karpatne, Levente J. Klein
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring. Big Data (2022)
Xinchao Liu, Kyongmin Yeo, Levente J. Klein, Youngdeok Hwang, Dzung Phan, Xiao Liu
Optimal Sensor Placement for Atmospheric Inverse Modelling. Big Data (2022)
Chulin Wang, Kyongmin Yeo, Xiao Jin, Andrés Codas, Levente J. Klein, Bruce Elmegreen
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process. CoRR (2021)
Kyongmin Yeo, Dylan E. C. Grullon, Fan-Keng Sun, Duane S. Boning, Jayant R. Kalagnanam
Variational Inference Formulation for a Model-Free Simulation of a Dynamical System with Unknown Parameters by a Recurrent Neural Network. SIAM J. Sci. Comput. 43 (2) (2021)
Kyongmin Yeo, Zan Li, Wesley M. Gifford
Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System. CoRR (2021)
Kyongmin Yeo, Dylan E. C. Grullon, Fan-Keng Sun, Duane S. Boning, Jayant R. Kalagnanam
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network. CoRR (2020)
Kyongmin Yeo
Data-driven Reconstruction of Nonlinear Dynamics from Sparse Observation. CoRR (2019)
Kyongmin Yeo
Data-driven reconstruction of nonlinear dynamics from sparse observation. J. Comput. Phys. 395 (2019)
Kyongmin Yeo, Igor Melnyk
Deep learning algorithm for data-driven simulation of noisy dynamical system. J. Comput. Phys. 376 (2019)
Kyongmin Yeo
Short note on the behavior of recurrent neural network for noisy dynamical system. CoRR (2019)
Youngdeok Hwang, Hang J. Kim, Won Chang, Kyongmin Yeo, Yongku Kim
Bayesian pollution source identification via an inverse physics model. Comput. Stat. Data Anal. 134 (2019)
Kyongmin Yeo, Igor Melnyk
Deep learning algorithm for data-driven simulation of noisy dynamical system. CoRR (2018)
Kyongmin Yeo, Youngdeok Hwang, Xiao Liu, Jayant Kalagnanam
Development of a spectral source inverse model by using generalized polynomial chaos. CoRR (2018)
Kyongmin Yeo, Igor Melnyk, Nam Nguyen, Eun Kyung Lee
DE-RNN: Forecasting the Probability Density Function of Nonlinear Time Series. ICDM (2018)
Kyongmin Yeo
Model-free prediction of noisy chaotic time series by deep learning. CoRR (2017)
Kyongmin Yeo, Martin R. Maxey
Simulation of concentrated suspensions using the force-coupling method. J. Comput. Phys. 229 (6) (2010)