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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.

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🌟 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 | Jooyoung Hahn (Slovak University of Technology) - Advanced Finite Volume Method for G-equation in Turbulent Combustion

MINDS SEMINAR

period : 2024-11-04 ~ 2024-11-04

time : 16:00:00 ~ 17:00:00

개최 장소 : Math Bldg 208 & Online streaming (Zoom)

Topic : Advanced Finite Volume Method for G-equation in Turbulent Combustion

개요

Date	2024-11-04 ~ 2024-11-04	Time	16:00:00 ~ 17:00:00
Speaker	Jooyoung Hahn	Affiliation	Slovak University of Technology
Place	Math Bldg 208 & Online streaming (Zoom)	Streaming link	ID : 688 896 1076 / PW : 54321
Topic	Advanced Finite Volume Method for G-equation in Turbulent Combustion
Contents	In this talk, we introduce a cell-centered finite volume method specifically designed to address the challenges of numerically solving the G-equation in premixed turbulent combustion scenarios. The G-equation, that is, level set equation, incorporates advective, normal, and curvature terms to model the f lame front propagation. The proposed algorithm, distinctively developed for polyhedral meshes, demonstrates significant advancements over traditional level set solvers. Key highlights of the approach from a numerical perspective include: 1. Enhanced Convergence: The method achieves second order convergence on polyhedral meshes when the temporal and spatial step sizes are reduced proportionally, showcasing improved accuracy in capturing com plex surface evolution. 2. Simplified Parallel Computing: Utilizing a domain decomposition strategy with a 1-ring face neighborhood structure, our method integrates seamlessly with existing cell-centered finite volume frameworks, facilitating straightforward parallel implementation. 3. Relaxed Time Step Constraints: By incorporating a semi-implicit scheme, our method mitigates the stringent time step limitations imposed by the CFL condition, enhancing computational efficiency. Throughout the presentation, we discuss thoroughly various numerical examples that validate the performance and numerical properties of the proposed algorithm, with a particular focus on handling each velocity term in the G-equation. The presentation will highlight the technical innovations and practical implications of our method in the context of general surface evolution. (It has received funding from the European Union ́s Horizon 2020 Research and Innovation Programme under the Programme SASPRO 2 COFUND Marie Sklodowska-Curie grant agreement No. 945478.)