Kyongmin Yeo

发表

Development of hp-inverse model by using generalized polynomial chaos

Xiao Liu, Youngdeok Hwang, Jayant Kalagnanam, 2018 .

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

Kyongmin Yeo, 2017, ArXiv.

Data-driven Reconstruction of Nonlinear Dynamics from Sparse Observation pdf

Kyongmin Yeo, K. Yeo, 2019, J. Comput. Phys..

Learning temporal evolution of probability distribution with Recurrent Neural Network

Igor Melnyk, Eun Kyung Lee, Kyongmin Yeo, 2018 .

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

Kyongmin Yeo, K. Yeo, 2019, ArXiv.

Bayesian pollution source identification via an inverse physics model

Youngdeok Hwang, Won Chang, Kyongmin Yeo, 2019, Comput. Stat. Data Anal..

A statistical modeling approach for spatio-temporal degradation data

Xiao Liu, Kyongmin Yeo, Jayant R. Kalagnanam, 2018 .

Simulation of noisy dynamical system by Deep Learning

Kyongmin Yeo, 2017 .

Modification of Near-Wall Turbulence by Stable Stratification

Kyongmin Yeo, Byung-Gu Kim, Changhoon Lee, 2006 .

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

Igor Melnyk, Nam Nguyen, Eun Kyung Lee, 2018, 2018 IEEE International Conference on Data Mining (ICDM).

Simulation of concentrated suspensions using the force-coupling method

Martin R. Maxey, Kyongmin Yeo, M. Maxey, 2010, J. Comput. Phys..

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

Igor Melnyk, Kyongmin Yeo, K. Yeo, 2018, J. Comput. Phys..

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

Xiao Liu, Youngdeok Hwang, Jayant Kalagnanam, 2018, ArXiv.

A statistical model for 1-hour- to 24-hour-ahead prediction of hourly ozone concentrations at ground level in Singapore

Youngdeok Hwang, J. Singh, Yasuo Amemiya, 2014 .

Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network

Duane S. Boning, Fan-Keng Sun, Kyongmin Yeo, 2020, SIAM J. Sci. Comput..

Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System pdf

Wesley M. Gifford, Kyongmin Yeo, Zan Li, 2021, ArXiv.

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

Levente J. Klein, Kyongmin Yeo, Andres Codas, 2021, ArXiv.

Dynamics of concentrated suspensions of non-colloidal particles in Couette flow

Martin R. Maxey, Kyongmin Yeo, M. Maxey, 2010, Journal of Fluid Mechanics.

Rheology and ordering transitions of non-Brownian suspensions in a confined shear flow: effects of external torques.

Kyongmin Yeo, M. Maxey, K. Yeo, 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

A statistical modeling approach for air quality data based on physical dispersion processes and its application to ozone modeling

Youngdeok Hwang, Kyongmin Yeo, Jayant R. Kalagnanam, 2016 .

Eulerian and Lagrangian statistics in stably stratified turbulent channel flows

Kyongmin Yeo, Changhoon Lee, Changhoon Lee, 2009 .

Collective dynamics in a binary mixture of hydrodynamically coupled microrotors.

Enkeleida Lushi, Kyongmin Yeo, Petia M Vlahovska, 2015, Physical review letters.

Modulation of homogeneous turbulence seeded with finite size bubbles or particles

Suchuan Dong, Martin R. Maxey, Kyongmin Yeo, 2010 .

Statistical-Physical Estimation of Pollution Emission

Youngdeok Hwang, Kyongmin Yeo, Emre Barut, 2018 .

Dynamic Self-Assembly of Spinning Particles

George Em Karniadakis, Martin R. Maxey, Kyongmin Yeo, 2007 .

On the near-wall characteristics of acceleration in turbulence

Kyongmin Yeo, Changhoon Lee, Changhoon Lee, 2010, Journal of Fluid Mechanics.

Measurement of Convective Entrainment Using Lagrangian Particles

Kyongmin Yeo, David M. Romps, K. Yeo, 2013 .