Kyongmin Yeo

According to our database, Kyongmin Yeo authored at least 19 papers between 2010 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Inverse Models for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes.

Xiao Liu, Kyongmin Yeo

Technometrics, July, 2023

A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network.

Takuya Kurihana, Kyongmin Yeo, Daniela Szwarcman, Bruce Elmegreen, S. Karthik Mukkavilli, Johannes Schmude, Levente J. Klein

CoRR, 2023

A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series.

Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Roman Vaculín

CoRR, 2023

An End-to-End Time Series Model for Simultaneous Imputation and Forecast.

Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Dzung T. Phan, Roman Vaculín, Jayant Kalagnanam

CoRR, 2023

2022

Generative Adversarial Network for Probabilistic Forecast of Random Dynamical Systems.

Kyongmin Yeo, Zan Li, Wesley M. Gifford

SIAM J. Sci. Comput., August, 2022

Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks.

Mykhaylo Zayats, Malgorzata J. Zimon, Kyongmin Yeo, Sergiy Zhuk

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Optimal Sensor Placement for Atmospheric Inverse Modelling.

Xinchao Liu, Kyongmin Yeo, Levente J. Klein, Youngdeok Hwang, Dzung Phan, Xiao Liu

Proceedings of the IEEE International Conference on Big Data, 2022

Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring.

Arka Daw, Kyongmin Yeo, Anuj Karpatne, Levente J. Klein

Proceedings of the IEEE International Conference on Big Data, 2022

2021

Variational Inference Formulation for a Model-Free Simulation of a Dynamical System with Unknown Parameters by a Recurrent Neural Network.

Kyongmin Yeo, Dylan E. C. Grullon, Fan-Keng Sun, Duane S. Boning, Jayant R. Kalagnanam

SIAM J. Sci. Comput., 2021

S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process.

Chulin Wang, Kyongmin Yeo, Xiao Jin, Andrés Codas, Levente J. Klein, Bruce Elmegreen

CoRR, 2021

Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System.

Kyongmin Yeo, Zan Li, Wesley M. Gifford

CoRR, 2021

2019

Deep learning algorithm for data-driven simulation of noisy dynamical system.

Kyongmin Yeo, Igor Melnyk

J. Comput. Phys., 2019

Data-driven reconstruction of nonlinear dynamics from sparse observation.

Kyongmin Yeo

J. Comput. Phys., 2019

Bayesian pollution source identification via an inverse physics model.

Youngdeok Hwang, Hang J. Kim, Won Chang, Kyongmin Yeo, Yongku Kim

Comput. Stat. Data Anal., 2019

Short note on the behavior of recurrent neural network for noisy dynamical system.

Kyongmin Yeo

CoRR, 2019

2018

Development of a spectral source inverse model by using generalized polynomial chaos.

Kyongmin Yeo, Youngdeok Hwang, Xiao Liu, Jayant Kalagnanam

CoRR, 2018

DE-RNN: Forecasting the Probability Density Function of Nonlinear Time Series.

Kyongmin Yeo, Igor Melnyk, Nam Nguyen, Eun Kyung Lee

Proceedings of the IEEE International Conference on Data Mining, 2018

2017

Model-free prediction of noisy chaotic time series by deep learning.

Kyongmin Yeo

CoRR, 2017

2010

Simulation of concentrated suspensions using the force-coupling method.

Kyongmin Yeo, Martin R. Maxey

J. Comput. Phys., 2010