COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |
University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > New harmonic-measure distribution functions of simply connected and multiply connected planar domains
New harmonic-measure distribution functions of simply connected and multiply connected planar domainsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact nobody. CATW04 - Complex analysis: techniques, applications and computations - perspectives in 2023 We can encode information about the shape of the boundary of a planar region Ω by keeping track of the exit locations of Brownian particles released from a basepoint z0 in Ω. Tracking for each r > 0 the probability that the Brownian particle first exits the region Ω via a boundary point of Ω within the closed disc B(z0, r) of radius r about z0 yields the (“global”) h-function or harmonic-measure distribution function h® = hΩ,z0®. By contrast, tracking the probability that the Brownian particle first exits the closed disc B(z0, r) via a point on the boundary ∂Ω of Ω, as opposed to via a point on ∂B(z0, r)\∂Ω, yields the (“local”) g-function g® = gΩ,z0®. I will report the recently calculated h-functions and g-functions of several simply connected regions Ω, as well as the h-functions of multiply connected domains whose boundary components may be circles, intervals or polygons. Tools used include conformal mapping, properties of harmonic measure, and the prime function. This is joint work with Arunmaran Mahenthiram, Byron Walden and Christopher Green. This talk is part of the Isaac Newton Institute Seminar Series series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsThe National Trust for Scotland Stem CellOther talksWelcome & Introduction Tea and Coffee Break Spin model of collective animal behavior: from whole group to a single animal and back Fact-checking as a conversation: an AI perspective Learning Operators From Data; Applications to Inverse Problems and Constitutive Modeling - Bayesian Inversion and Surrogate Modeling Patient-specific, organ-scale forecasting of prostate cancer growth in active surveillance |