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강연자 홍영준
소속 성균관대학교
date 2023-04-13


This lecture explores the topics and areas that have guided my research in computational mathematics and deep learning in recent years. Numerical methods in computational science are essential for comprehending real-world phenomena, and deep neural networks have achieved state-of-the-art results in a range of fields. The rapid expansion and outstanding success of deep learning and scientific computing have led to their applications across multiple disciplines. In this lecture, I will focus on connecting machine learning with applied mathematics, specifically discussing topics such as adversarial examples, generative models, and scientific machine learning.


첨부 '1'
  1. Universality of log-correlated fields

  2. <학부생을 위한 ɛ 강연> 양자상태의 기하학

  3. Class field theory for 3-dimensional foliated dynamical systems

  4. Satellite operators on knot concordance

  5. <정년퇴임 기념강연> 작용소대수와 양자정보이론

  6. Entropy of symplectic automorphisms

  7. Equations defining algebraic curves and their tangent and secant varieties

  8. Descent in derived algebraic geometry

  9. 14Apr
    by 김수현
    in 수학강연회

    Toward bridging a connection between machine learning and applied mathematics

  10. Vlasov-Maxwell equations and the Dynamics of Plasmas

  11. Study stochastic biochemical systems via their underlying network structures

  12. <학부생을 위한 ɛ 강연> 복잡한 생명현상을 위한 21세기 현미경, 수학!

  13. Birational Geometry of varieties with effective anti-canonical divisors

  14. Contact instantons and entanglement of Legendrian links

  15. <학부생을 위한 ɛ 강연> Self-Supervised Learning in Computer Vision

  16. <학부생을 위한 강연> 수학과 보험산업

  17. Counting number fields and its applications

  18. Towards Trustworthy Scientific Machine Learning: Theory, Algorithms, and Applications

  19. Elliptic equations with singular drifts in critical spaces

  20. Contact topology of singularities and symplectic fillings

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