Our Mission

We are committed to advancing fundamental science and high-impact processing technologies that enable breakthrough materials and predictive processes for next-generation semiconductor manufacturing.

Chaotic Advection & Nonlinear Fluid Dynamics

We pursue first-principles research in theoretical fluid dynamics, focusing on chaotic advection, Lagrangian transport, and nonlinear flow instability.
By identifying coherent structures, transport barriers, and strange attractors, we establish a fundamental theoretical framework for understanding mixing, transport, and reaction in complex flows.

Data-Driven Modeling & Machine Learning

We believe data is not a replacement for theory, but a lens to reveal it.
Our research combines physics-based computation and machine learning to extract governing patterns, reduced representations, and surrogate dynamics, enabling interpretable and predictive modeling of complex material and flow systems.

Spectral Element Method–Based High-Accuracy CFD

We develop high-order computational fluid dynamics grounded in numerical analysis and continuum mechanics. By advancing the Spectral Element Method, our research combines geometric flexibility with spectral-level accuracy, enabling the faithful resolution of multiscale transport phenomena, sharp interfaces, and nonlinear flow dynamics in complex systems.

News

Research

Project

Ongoing + Completed +

Publications

Effect of 3D-printed surface textures on wear mechanism in 3-body abrasion of soil

Yong Seok Choi, Seongmin Kim, Deaho Lee, Jungkil Shim, Amir Asadi, Jun Qu, Ho Jun Kim, Kyungjun Lee
15 January 2026
Link

Dependence of optimal conditions for abundant O(1 D) surface flux on the source gas ratio and pressure of O2/Ar inductively coupled plasmas

Sanghyun Jo, Cheongbin Cheon, Hae June Lee, Ho Jun Kim
28 November 2025
Link

Multi-Scale Computational Insights into SinH2n+1 Adsorption in SiH4/H2/Ar Plasma Environments

Hwanyeol Park, Ho Jun Kim
01 May 2025
Link

Evaluation and prediction of superhydrophobic surface durability using rolling wear tests and finite element analysis

Kyeongryeol Park, Hyunjong Kim, Kihwan Kim, Changwoo Lee, Amir Asadi, Ho Jun Kim, Kyungjun Lee
20 April 2025
Link

Accelerating hydrogen evolution catalyst discovery via data-driven strategy for high-performance single-atom catalysts embedded in h-BN

Hwanyeol Park, Dae-Myeong Geum, Ho Jun Kim
15 April 2025
Link

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