초청 강연

Deep Learning Lecture Series

일시:  2023년 7월 4일(화) ~ 5일(수)

장소:  경북대학교 자연과학대학 213호

연사:  이재용 박사님(고등과학원)

주제:  Implementing Physics-Informed Neural Networks(PINNs) and Deep Operator Networs(DeepONets) for Scientific Date Modeling

초록:  This tutorial aims to provide an introductory exploration of Physics-Informed Neural Networks (PINNs) for solving partial differential equations (PDEs) and Deep Operator Networks (DeepONets) for operator learning. The tutorial will guide you through a step-by-step process of implementing PINNs and DeepONets using Python and popular deep learning library PyTorch. It will cover the fundamental concepts of PINNs for PDE solving and DeepONets for learning complex operators, demonstrating their capabilities and potential in scientific data modeling tasks.

일시:  2023년 09월 04일 (월) 17:00~18:00

장소:  경북대학교 자연과학대학 319

연사:  Masahiro Suzuki 교수님(Nagoya Institute of Technology)

주제: Solitary waves of the Vlasov-Poisson system

초록:  We consider the Vlasov-Poisson system describing a two-species plasma with spatial dimension 1 and the velocity variable in $\mathbb{R}^n$. We find the necessary and sufficient conditions for the existence of solitary waves of the system. To this end, we need to investigate the distribution of ions trapped by the electrostatic potential. Furthermore, we classify completely in all possible cases whether or not the solitary wave is unique, when we exclude the variant caused by translation. There are both cases that it is unique and nonunique.

일시: 2024년 2월 19일 (월) 16:00~17:00

장소: 자연과학대학 213호

연사: 김수현 박사님 (부산대학교 수학과)

주제: Gauss–Legendre polynomials for parametric curve shape adjustment

초록: Controlling the deformation of polynomial curves has significant importance in various domains, including computer graphics and computer-aided design (CAM/CAD/CAGD). One widely-used expression form of a poly-nomial curve is the B´ezier curve, which is constructed by barycentric combination of the Bernstein polynomial basis with the given control points. In this presentation, we will introduce the Gauss–Legendre polynomial, originally derived from the process of developing as the rectifying control polygon of Pythagorean hodograph(PH) curves. Gauss-Legendre polynomials have the same length as the arc length of the corresponding PH curve. If this concept is extended to a polynomial curve, the shape of the curve can be easily predicted and adjusted using the proposed control polygon, even for high-degree polynomial curves. Similar to how a B´ezier curve relies on Bernstein polynomials for its expression, a PH curve can be expressed through a linear combi-nation of Gauss–Legendre control points, with coefficients determined by Gauss–Legendre polynomials. These polynomials can be constructed from Lagrange interpolators defined over the roots of Legendre polynomials. This research is joint work with Hwan Pyo Moon and Song-Hwa Kwon.



일시: 2024년 2월 19일 (월) 16:00~17:00

장소: 자연과학대학 213호

연사: 이현주 연구원 (연세대학교의료원 영상의학과)

주제: Decision-making approach to enhance the performance of neural networks for medical image classification

초록:  In this study, we propose a simple but effective method to improve the performance of transfer learning-based networks for medical imaging on given data. The proposed method has two steps. The Original network is used for initial decision-making, and some of its decisions are passed to a second decision-making stage involving a new network called the True network. A True network has the same architecture as the Original network but is trained only on a subset of the original data that meets certain conditions. It is then used to validate the classification results of the Original network, and misclassified images are identified and reclassified. The effectiveness of this approach was evaluated on thyroid nodule ultrasound images using the ResNet101 architecture as the representative network. The proposed method enhanced performance in all five metrics, including accuracy, sensitivity, specificity, f1-score, and AUC compared to using only the Original network or True network. Additional experiments showed improved performance of several other CNNs for the medical images as well. Our proposed method improves the performance of transfer learning-based networks in medical imaging without additional data, even when processing limited amounts of data. The approach proposed can potentially increase the accuracy of medical image classification, making it a promising technology for medical image analysis.



GNA with Applications

일시:  2024년 3월 20일 (수) 15:00~17:00

장소:  자연과학대학 213호

연사: 최우진 명예교수 (KAIST 수리과학과)

강의내용: NeurIPS 2014에서 발표된 Goodfellowetal의 "Generative Adversarial Nets"는  Game Theory에 기반한 Optimization Problem을 구성하여 Gradient Iteration으로 생성모델을 구현합니다. GAN은 앞서 소개한AEVB학습과정에 나타나는 Sampling이 없으므로 생성속도가 빠른 장점이 있습니다. 

일시:  2024년 5월 24일 () 15:00~15:50 / 16:00~16:50

장소:  자연과학대학 219

연사: 강문진 교수님 (KAIST)

강의내용:  The Cauchy problem for compressible Euler system from inviscid limit of Navier-Stokes remains completely open, as a challenging issue in fluid dynamics. This lectures focus on the 1D isentropic case for the open problem. We will present the global well-posedness of entropy solutions with small BV initial data in the class of inviscid limits from the associate Navier-Stokes. More precisely, any small BV entropy solutions are inviscid limits from Navier-Stokes. Those are unique and stable among inviscid limits from Navier-Stokes. The proof is based on the three main methodologies: the modified front tracking algorithm; the a-contraction with shifts; the method of compensated compactness. In the first lecture, I will introduce the method of a-contraction with shifts. In the second lecture, I will present the proof for the well-posedness of Cauchy problem.