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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260911T100000
DTEND;TZID=Asia/Seoul:20260911T120000
DTSTAMP:20260821T043336Z
CREATED:20260821T043336Z
LAST-MODIFIED:20260821T043336Z
UID:12848-1789120800-1789128000@www.ibs.re.kr
SUMMARY:Learning to learn ecosystems from limited data - Hyeong Jun Jang
DESCRIPTION:In this talk\, we discuss the paper “Learning to learn ecosystems from limited data” by Zheng-Meng Zhai et al.\, PNAS\, 2025. \nAbstract: \nA fundamental challenge in developing data-driven approaches to ecological systems for tasks such as state estimation and prediction is the paucity of the observational or measurement data. For example\, modern machine-learning techniques such as deep learning or reservoir computing typically require a large quantity of data. Leveraging synthetic data from paradigmatic nonlinear but non-ecological dynamical systems\, we develop a meta-learning framework with time-delayed feedforward neural networks to predict the long-term behaviors of ecological systems as characterized by their attractors. We show that the framework is capable of accurately reconstructing the “dynamical climate” of the ecological system with limited data. Three benchmark population models in ecology\, namely the Hastings-Powell model\, its variant\, and the Lotka-Volterra system\, are used to demonstrate the performance of the meta-learning based prediction framework. In all cases\, enhanced accuracy and robustness have been achieved using five to seven times less training data as compared with the corresponding machine-learning method trained solely from the ecosystem data. In addition\, two real-world ecological benchmark datasets: the microbial time-series dataset and global population dynamics database\, are tested to demonstrate the applicability of the meta-learning framework to the real world. A number of issues affecting the prediction performance are addressed.
URL:https://www.ibs.re.kr/bimag/event/learning-to-learn-ecosystems-from-limited-data-hyeong-jun-jang/
LOCATION:109\, Conference room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20261002T100000
DTEND;TZID=Asia/Seoul:20261002T120000
DTSTAMP:20260824T001413Z
CREATED:20260824T001413Z
LAST-MODIFIED:20260824T001413Z
UID:12985-1790935200-1790942400@www.ibs.re.kr
SUMMARY:Ptolemy's Equant Equates to a Universal Dynamical Clock via Machine Learning - Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Ptolemy’s Equant Equates to a Universal Dynamical Clock via Machine Learning” by Jindong Zhang et al.\, arXiv\, 2026. \nAbstract: \nOscillatory dynamics arise ubiquitously in nonlinear systems\, yet identifying a physically interpretable phase and phase dynamics in nonlinear\, high-dimensional oscillations remains a central unresolved problem. Here we establish the principle of a universal dynamical clock\, a physical perspective in which oscillations of arbitrary dimensionality and geometry are equivalently represented as uniform rotation through an equant-induced nonlinear viewing coordinate\, inspired by Ptolemy’s equant and formalised through an areal-uniformity principle reminiscent of Kepler’s second law. Using a machine-learning framework\, we demonstrate the existence of such an equant for a broad class of oscillatory dynamics and construct the associated dynamical clock and phase dynamics under additive forces\, including noise\, periodic perturbations\, and coupling. Its value in uncovering new physical rules and phenomena is demonstrated by four findings: (i) collective oscillations in Escherichia coli populations obey a previously unexplained superlinear scaling law\, resolving a long-standing open problem posed in 2004; (ii) the response mechanisms of engineered genetic circuits to changes in gene expression and environmental conditions; (iii) a classical-mechanics counterpart of the Berry geometric phase emerges naturally from the phase of the dynamical clock; and (iv) optimal equant non-uniformity provides a geometric early-warning signal for critical transitions and enables prediction of critical parameters. By providing operational and system-agnostic phase dynamics that can be constructed directly from data\, the dynamical clock enables principled classification\, comparison\, and control of oscillatory systems\, and offers a new route to understanding how specific dynamical regimes support distinct functional behaviours in networked systems
URL:https://www.ibs.re.kr/bimag/event/ptolemys-equant-equates-to-a-universal-dynamical-clock-via-machine-learning-gyuyoung-hwang/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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