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2020 – today
- 2023
[j7]Xiao Liu
, Kyongmin Yeo:
Inverse Models for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes. Technometrics 65(3): 432-445 (2023)- [i13]Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Dzung T. Phan, Roman Vaculín, Jayant Kalagnanam:
An End-to-End Time Series Model for Simultaneous Imputation and Forecast. CoRR abs/2306.00778 (2023) - [i12]Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo, Nam Nguyen, Roman Vaculín:
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series. CoRR abs/2311.12290 (2023) - [i11]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 abs/2312.13212 (2023) - 2022
- [j6]Kyongmin Yeo, Zan Li, Wesley M. Gifford:
Generative Adversarial Network for Probabilistic Forecast of Random Dynamical Systems. SIAM J. Sci. Comput. 44(4): 2150- (2022) - [c4]Arka Daw, Kyongmin Yeo, Anuj Karpatne, Levente J. Klein:
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring. IEEE Big Data 2022: 4835-4841 - [c3]Xinchao Liu, Kyongmin Yeo, Levente J. Klein, Youngdeok Hwang, Dzung Phan, Xiao Liu:
Optimal Sensor Placement for Atmospheric Inverse Modelling. IEEE Big Data 2022: 4848-4853 - [c2]Mykhaylo Zayats, Malgorzata J. Zimon, Kyongmin Yeo, Sergiy Zhuk:
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks. CDC 2022: 3377-3382 - [i10]Mykhaylo Zayats, Malgorzata J. Zimon, Kyongmin Yeo, Sergiy Zhuk:
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks. CoRR abs/2204.07413 (2022) - [i9]Arka Daw, Kyongmin Yeo, Anuj Karpatne, Levente J. Klein:
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring. CoRR abs/2211.00864 (2022) - 2021
- [j5]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): A1305-A1335 (2021) - [i8]Kyongmin Yeo, Zan Li, Wesley M. Gifford:
Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System. CoRR abs/2111.03126 (2021) - [i7]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 abs/2111.04639 (2021) - 2020
- [i6]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 abs/2003.01184 (2020)
2010 – 2019
- 2019
- [j4]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: 76-92 (2019) - [j3]Kyongmin Yeo, Igor Melnyk:
Deep learning algorithm for data-driven simulation of noisy dynamical system. J. Comput. Phys. 376: 1212-1231 (2019) - [j2]Kyongmin Yeo:
Data-driven reconstruction of nonlinear dynamics from sparse observation. J. Comput. Phys. 395: 671-689 (2019) - [i5]Kyongmin Yeo:
Short note on the behavior of recurrent neural network for noisy dynamical system. CoRR abs/1904.05158 (2019) - [i4]Kyongmin Yeo:
Data-driven Reconstruction of Nonlinear Dynamics from Sparse Observation. CoRR abs/1906.04059 (2019) - 2018
- [c1]Kyongmin Yeo, Igor Melnyk, Nam Nguyen, Eun Kyung Lee:
DE-RNN: Forecasting the Probability Density Function of Nonlinear Time Series. ICDM 2018: 697-706 - [i3]Kyongmin Yeo, Youngdeok Hwang, Xiao Liu, Jayant Kalagnanam:
Development of a spectral source inverse model by using generalized polynomial chaos. CoRR abs/1801.03009 (2018) - [i2]Kyongmin Yeo, Igor Melnyk:
Deep learning algorithm for data-driven simulation of noisy dynamical system. CoRR abs/1802.08323 (2018) - 2017
- [i1]Kyongmin Yeo:
Model-free prediction of noisy chaotic time series by deep learning. CoRR abs/1710.01693 (2017) - 2010
- [j1]Kyongmin Yeo, Martin R. Maxey:
Simulation of concentrated suspensions using the force-coupling method. J. Comput. Phys. 229(6): 2401-2421 (2010)
Coauthor Index
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