Abstract: A central challenge of modern dynamical systems theory is to make nonlinear systems tractable without sacrificing fidelity. Koopman operator theory pursues this goal by lifting nonlinear dynamics into a linear, but infinite dimensional, operator acting on a function space. This operator-theoretic perspective underlies a broad class of modern data-driven methods, from dynamic mode decomposition …
Events
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In this talk, we discuss the paper “Fast training of accurate physics-informed neural networks without gradient descent” by Chinmay Datar et al., ICLR, 2026. Abstract: Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a promising framework for approximating PDE solutions, their … |
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Abstract: In general, the rates of infection and removal (whether through recovery or death) are nonlinear functions of the number of infected and susceptible individuals. One of the simplest models for the spread of infectious diseases is the SIR model, which categorizes individuals as susceptible, infectious, recovered or deceased. In this model, the infection rate, … |
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In this talk, we discuss the paper "AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML" by Patara Trirat et al, ICML, 2025. Abstract: Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems often require technical expertise to set up … |
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In this talk, we discuss the paper "Evolution of error correction through a need for speed" by Riccardo Ravasio et al., Science, 2026. Abstract: Kinetic proofreading is a class of error-correcting mechanisms in biology that expend energy to avoid mistakes during replication, transcription, and translation. Proofreading is typically assumed to evolve when selection for fidelity … |
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이번 세미나에서는 "조선이 만난 아인슈타인", "판타 레이" 등을 저술하신 민태기 소장님을 모시고 "수학이 공학의 언어라면"이라는 주제로 강연을 진행할 예정입니다. |
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In this talk, we discuss the paper "Tangent-Space Regularization for Neural-Network Models of Dynamical Systems" by Fredrik B Carlson et al, arXiv (2026). Abstract: This work introduces the concept of tangent space regularization for neural-network models of dynamical systems. The tangent space to the dynamics function of many physical systems of interest in control applications … |
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In this talk, we discuss the paper "Towards a General Intelligence and Interface for Wearable Health Data" by Girish Narayanswamy et al., arXiv, 2026. Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging. Specifically, converting low-level sensor data into representations capable … |
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