Minseok Choi

发表

A convergence study for SPDEs using combined Polynomial Chaos and Dynamically-Orthogonal schemes

Minseok Choi, George E. Karniadakis, Themistoklis P. Sapsis, 2013, J. Comput. Phys..

Bayesian deep learning framework for uncertainty quantification in high dimensions

Minseok Choi, Jeahan Jung, 2022, ArXiv.

MGDGAN: Multiple Generator and Discriminator Generative Adversarial Networks for Solving Stochastic Partial Differential Equations

Minseok Choi, Sungha Cho, S. Cho, 2022, IEEE Access.

A robust bi-orthogonal/dynamically-orthogonal method using the covariance pseudo-inverse with application to stochastic flow problems

Minseok Choi, Hessam Babaee, George E. Karniadakis, 2017, J. Comput. Phys..

On the equivalence of dynamically orthogonal and bi-orthogonal methods: Theory and numerical simulations

Minseok Choi, George E. Karniadakis, Themistoklis P. Sapsis, 2014, J. Comput. Phys..

Adaptive ANOVA decomposition of stochastic incompressible and compressible flows

Xiu Yang, Guang Lin, Minseok Choi, 2012, J. Comput. Phys..

Error Estimates for the ANOVA Method with Polynomial Chaos Interpolation: Tensor Product Functions

Minseok Choi, Zhongqiang Zhang, George E. Karniadakis, 2012, SIAM J. Sci. Comput..

Physics-informed variational inference for uncertainty quantification of stochastic differential equations

Minseok Choi, Hyomin Shin, 2023, J. Comput. Phys..

Anchor Points Matter in ANOVA Decomposition

George Em Karniadakis, Zhongqiang Zhang, G. Karniadakis, 2011 .

A video-based SlowFastMTB model for detection of small amounts of smoke from incipient forest fires

Minseok Choi, Hyunseok Oh, C. Kim, 2022, J. Comput. Des. Eng..

Dimension reduction in heterogeneous neural networks: Generalized Polynomial Chaos (gPC) and ANalysis-Of-VAriance (ANOVA)

Minseok Choi, Ioannis G. Kevrekidis, Carlo R. Laing, 2016, 1603.06643.

Data-Driven Method to Quantify Correlated Uncertainties

Minseok Choi, Jeahan Jung, 2023, IEEE Access.