Simulation of concentrated suspensions using the force-coupling method. - Kyongmin Yeo; Martin R. Maxey (J. Comput. Phys., 2010)

Model-free prediction of noisy chaotic time series by deep learning. - Kyongmin Yeo (CoRR, 2017)

DE-RNN: Forecasting the Probability Density Function of Nonlinear Time Series. - Kyongmin Yeo; Igor Melnyk; Nam Nguyen; Eun Kyung Lee (ICDM, 2018)

Development of a spectral source inverse model by using generalized polynomial chaos. - Kyongmin Yeo; Youngdeok Hwang; Xiao Liu 044; Jayant Kalagnanam (CoRR, 2018)

Short note on the behavior of recurrent neural network for noisy dynamical system. - Kyongmin Yeo (CoRR, 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)

Data-driven reconstruction of nonlinear dynamics from sparse observation. - Kyongmin Yeo (J. Comput. Phys., 2019)

Deep learning algorithm for data-driven simulation of noisy dynamical system. - Kyongmin Yeo; Igor Melnyk (J. Comput. Phys., 2019)

Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System. - Kyongmin Yeo; Zan Li; Wesley M. Gifford (CoRR, 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)

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)

Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring. - Arka Daw; Kyongmin Yeo; Anuj Karpatne; Levente J. Klein (IEEE Big Data, 2022)

Optimal Sensor Placement for Atmospheric Inverse Modelling. - Xinchao Liu; Kyongmin Yeo; Levente J. Klein; Youngdeok Hwang; Dzung Phan; Xiao Liu 044 (IEEE Big Data, 2022)

Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks. - Mykhaylo Zayats; Malgorzata J. Zimon; Kyongmin Yeo; Sergiy Zhuk (CDC, 2022)

Generative Adversarial Network for Probabilistic Forecast of Random Dynamical Systems. - Kyongmin Yeo; Zan Li; Wesley M. Gifford (SIAM J. Sci. Comput., 2022)

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)

A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series. - Trang H. Tran; Lam M. Nguyen; Kyongmin Yeo; Nam Nguyen; Roman Vaculín (CoRR, 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)

Inverse Models for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes. - Xiao Liu 044; Kyongmin Yeo (Technometrics, 2023)

Cascading Failure Prediction via Causal Inference. - Shiuli Subhra Ghosh; Anmol Dwivedi; Ali Tajer; Kyongmin Yeo; Wesley M. Gifford (CoRR, 2024)

CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation. - Claudius Krause; Michele Faucci Giannelli; Gregor Kasieczka; Benjamin Nachman; Dalila Salamani; David Shih; Anna Zaborowska; Oz Amram; Kerstin Borras; Matthew R. Buckley; Erik Buhmann; Thorsten Buss; et al. (CoRR, 2024)

Quantitative ultrasound augmented with machine learning to assess tissue microstructure during wound healing. - David Lemonnier; Brandon J. Sumpio; Maxwell Crouse; Ikram Mezghani; Georgios Theocharidis; Margaret A. Jakus; Karen Rayappa; Naoki Abe; Kyongmin Yeo; Tengfei Ma; Samuel K. Sia; Aristidis Veves; et al. (Biomed. Signal Process. Control., 2025)