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.
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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
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Schedule
MINDS SEMINAR
From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus?
MINDS SEMINAR
period : 2023-05-03 ~ 2023-05-03
time : 09:30:00 ~ 11:00:00
개최 장소 : Online streaming (Zoom)
Topic : From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus?
개요
| Date | 2023-05-03 ~ 2023-05-03 | Time | 09:30:00 ~ 11:00:00 |
| Speaker | George Em Karniadakis | Affiliation | The Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering, Brown University |
| Place | Online streaming (Zoom) | Streaming link | ID : 688 896 1076 / PW : 54321 |
| Topic | From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus? | ||
| Contents | We will review physics-informed neural networks (NNs) and summarize available extensions for applications in computational science and engineering. We will also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification. The universal approximation theorem of operators is suggestive of the potential of NNs in learning from scattered data any continuous operator or complex system. We first generalize the theorem to deep neural networks, and subsequently we apply it to design a new composite NN with small generalization error, the deep operator network (DeepONet), consisting of a NN for encoding the discrete input function space (branch net) and another NN for encoding the domain of the output functions (trunk net). We demonstrate that DeepONet can learn various explicit operators, e.g., integrals, Laplace transforms and fractional Laplacians, as well as implicit operators that represent deterministic and stochastic differential equations. More generally, DeepOnet can learn multiscale operators spanning across many scales and trained by diverse sources of data simultaneously. Finally, we will present first results on the next generation of these architectures to biologically plausible designs based on spiking neural networks and Hebbian learning that are more efficient and closer to human intelligence. | ||
MinDS
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2023-04-18 09:17 ·
Views 1169 POSTECH SIAM Student Chapter
🌟 DACON Ranker Special Lecture: Winning Strategies for AI Competitions 🌟
2023 POSTECH & Peking SIAM Student Chapter Joint Conference
2023 PSSC Summer Camp
2022 PSSC Summer Camp
2022 POSTECH & Peking SIAM Student Chapter Joint Conference
MINDS-MoNET-ISE Workshop
Information, Network & Topological Data Analysis
2021 POSTECH MINDS WORKSHOP
Recent Progress in Data Science and Applications
- Nov. 19(Fri) ~ Nov. 20(Sat) 2021 (1 Night 2 Days)
- Workshop homepage
Fall 2021 Seminar Series
MINDS Seminar Series on Data Science, Machine Learning, and Scientific Computing
Every Tuesdays 05:00 PM
ILJU POSTECH MINDS Workshop on TDA and ML
July 6 ~ July 9
Registration is required (please register here)
POSTECH SIAM Student Chapter
