Welcome to MINDS!

Established in 2020, POSTECH Mathematical Institute for Data Science (MINDS) is the community of researchers in the areas of fundamental data science, machine learning, artificial intelligence, scientific computing, and humanitarian data science. MINDS mission is to provide a platform for collaboration among researchers and to provide various opportunities for students in data science. MINDS also aims to use our data science research to serve our local and global communities pursuing humanitarian data science.

News

🌟 DACON Ranker Special Lecture: Winning Strategies for AI Competitions 🌟 2023.11.30

3nd POSTECH&Peking SIAM Student Chapter Joint Conference 2023.11.01

2023 PSSC Summer Camp 2023.10.04

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고]-상시모집 2023.07.25

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.07.14

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구교수 채용 공고] 2023.06.12

Seminar | Joint seminar for probability and mathematical biology 2023.05.02

[POSTECH(포항공과대학교) 수리 데이터과학 연구소 연구계약직 공고] 2023.02.15

Upcoming Events

Schedule

MINDS SEMINAR

MINDS Seminar Series | Changho Kim (UC Merced) - Stochastic Multiscale Simulation Methods for Reactive Microfluids and Heterogeneous Catalysts

MINDS SEMINAR

period : 2022-05-24 ~ 2022-05-24

time : 10:00:00 ~ 11:00:00

개최 장소 : Online streaming (Zoom)

Topic : Stochastic Multiscale Simulation Methods for Reactive Microfluids and Heterogeneous Catalysts

개요

Date	2022-05-24 ~ 2022-05-24	Time	10:00:00 ~ 11:00:00
Speaker	Changho Kim	Affiliation	UC Merced
Place	Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Stochastic Multiscale Simulation Methods for Reactive Microfluids and Heterogeneous Catalysts
Contents	At the macroscale, fluids are typically modeled using the traditional Navier-Stokes equations, in which the underlying fluid particles are abstracted as continuous fields. While this approach is computationally efficient, it neglects the effects of thermal fluctuations. At the microscale, methods such as molecular dynamics (MD) explicitly simulate the underlying particles, capturing these effects at the cost of much greater computational expense. In between these two scales (i.e. the mesoscale), a range of problems exist which are small enough that thermal fluctuations play an important role, but too large to efficiently simulate using particle-based techniques. At this scale, fluctuating hydrodynamics (FHD) provides an approach which is relatively computationally efficient, but still incorporates the effects of thermal fluctuations. The FHD approach has been successfully applied to this type of problem. In this talk, I will describe how the FHD approach can be extended to two classes of stochastic chemical systems--reactive microfluids and heterogeneous catalysts--where both hydrodynamics and chemistry play essential roles. First, to construct numerical methods for reactive microfluids, I will explain how to incorporate various stochastic chemistry descriptions to the stochastic PDE description of FHD. Second, for small-scale heterogeneous catalyst applications, I will describe a hybrid simulation methodology that combines both the kinetic Monte Carlo (KMC) approach for surface chemistry and the FHD approach for hydrodynamic transport in the gas phase. I will also explain high-performance computing (HPC) implementations.