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過去の記録 ～07/02｜本日 07/03 | 今後の予定 07/04～
2025年07月03日(木)
応用解析セミナー
16:00-17:30 数理科学研究科棟(駒場) 128号室
確率論セミナーと合同開催
Jessica Lin 氏 (McGill University)
Generalized Front Propagation for Stochastic Spatial Models (English)
確率論セミナーと合同開催
Jessica Lin 氏 (McGill University)
Generalized Front Propagation for Stochastic Spatial Models (English)
[ 講演概要 ]
In this talk, I will present a general framework which can be used to analyze the scaling limits of various stochastic spatial "population" models. Such models include ternary Branching Brownian motion subject to majority voting and several interacting particle systems motivated by biology. The approach is based on moment duality and a PDE methodology introduced by Barles and Souganidis, which can be used to study the asymptotic behaviour of rescaled reaction-diffusion equations. In the limit, the models exhibit phase separation with an evolving interface which is governed by a global-in-time, generalized notion of mean-curvature flow. This talk is based on joint work with Thomas Hughes (University of Bath).
In this talk, I will present a general framework which can be used to analyze the scaling limits of various stochastic spatial "population" models. Such models include ternary Branching Brownian motion subject to majority voting and several interacting particle systems motivated by biology. The approach is based on moment duality and a PDE methodology introduced by Barles and Souganidis, which can be used to study the asymptotic behaviour of rescaled reaction-diffusion equations. In the limit, the models exhibit phase separation with an evolving interface which is governed by a global-in-time, generalized notion of mean-curvature flow. This talk is based on joint work with Thomas Hughes (University of Bath).
東京確率論セミナー
16:00-17:30 数理科学研究科棟(駒場) 128号室
教室は１２8です。応用解析セミナーとの合同セミナーです。 今日はTea Time はありません。
Jessica Lin 氏 (McGill University)
Generalized Front Propagation for Stochastic Spatial Models
教室は１２8です。応用解析セミナーとの合同セミナーです。 今日はTea Time はありません。
Jessica Lin 氏 (McGill University)
Generalized Front Propagation for Stochastic Spatial Models
[ 講演概要 ]
In this talk, I will present a general framework which can be used to analyze the scaling limits of various stochastic spatial "population" models. Such models include ternary Branching Brownian motion subject to majority voting and several interacting particle systems motivated by biology. The approach is based on moment duality and a PDE methodology introduced by Barles and Souganidis, which can be used to study the asymptotic behaviour of rescaled reaction-diffusion equations. In the limit, the models exhibit phase separation with an evolving interface which is governed by a global-in-time, generalized notion of mean-curvature flow. This talk is based on joint work with Thomas Hughes (University of Bath).
In this talk, I will present a general framework which can be used to analyze the scaling limits of various stochastic spatial "population" models. Such models include ternary Branching Brownian motion subject to majority voting and several interacting particle systems motivated by biology. The approach is based on moment duality and a PDE methodology introduced by Barles and Souganidis, which can be used to study the asymptotic behaviour of rescaled reaction-diffusion equations. In the limit, the models exhibit phase separation with an evolving interface which is governed by a global-in-time, generalized notion of mean-curvature flow. This talk is based on joint work with Thomas Hughes (University of Bath).
