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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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MINDS Seminar Series | Yeonjong Shin (KAIST) - Towards Trustworthy Scientific Machine Learning: Theory, Algorithms, and Applications

period : 2022-12-13 ~ 2022-12-13
time : 17:00:00 ~ 18:00
개최 장소 : Math Bldg 404 & Online streaming (Zoom)
Topic : Towards Trustworthy Scientific Machine Learning: Theory, Algorithms, and Applications
개요
Date 2022-12-13 ~ 2022-12-13 Time 17:00:00 ~ 18:00
Speaker Yeonjong Shin Affiliation KAIST
Place Math Bldg 404 & Online streaming (Zoom) Streaming link ID : 688 896 1076 / PW : 54321
Topic Towards Trustworthy Scientific Machine Learning: Theory, Algorithms, and Applications
Contents Machine learning (ML) has achieved unprecedented empirical success in diverse applications. It now has been applied to solve scientific problems, which has become an emerging field, Scientific Machine Learning (SciML). Many ML techniques, however, are very complex and sophisticated, commonly requiring many trial-and-error and tricks. These result in a lack of robustness and interpretability, which are critical factors for scientific applications. This talk centers around mathematical approaches for SciML, promoting trustworthiness. The first part is about how to embed physics into neural networks (NNs). I will present a general framework for designing NNs that obey the first and second laws of thermodynamics. The framework not only provides flexible ways of leveraging available physics information but also results in expressive NN architectures. The second part is about the training of NNs, one of the biggest challenges in ML. I will present an efficient training method for NNs - Active Neuron Least Squares (ANLS). ANLS is developed from the insight gained from the analysis of gradient descent training.
MinDS MinDS · 2022-10-04 10:27 · Views 501

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