136 citations · 353 across the 16 of their papers we have counts for
16 papers
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo +4
Time series forecasting using historical data has been an interesting and challenging topic, especially when the data is corrupted by missing values. In many industrial problem, it…
Inverse Models for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes
Xiao Liu, Kyongmin Yeo
Inverse problems involve making inference about unknown parameters of a physical process using observational data. This paper investigates an important class of inverse problems --…
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring
Arka Daw, Kyongmin Yeo, Anuj Karpatne +1
Inferring the source information of greenhouse gases, such as methane, from spatially sparse sensor observations is an essential element in mitigating climate change. While it is w…
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks
Mykhaylo Zayats, Małgorzata J. Zimoń, Kyongmin Yeo +1
We propose a new design of a neural network for solving a zero shot super resolution problem for turbulent flows. We embed Luenberger-type observer into the network's architecture…
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process
Chulin Wang, Kyongmin Yeo, Xiao Jin +3
We present a super-resolution model for an advection-diffusion process with limited information. While most of the super-resolution models assume high-resolution (HR) ground-truth…
Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System
Kyongmin Yeo, Zan Li, Wesley M. Gifford
We present a deep learning model for data-driven simulations of random dynamical systems without a distributional assumption. The deep learning model consists of a recurrent neural…