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UID:934@fds.yale.edu
DTSTART;TZID=America/New_York:20260213T130000
DTEND;TZID=America/New_York:20260213T140000
DTSTAMP:20260130T193053Z
URL:https://fds.yale.edu/events/fds-seminar-jonah-botvinick-greenhouse-cor
 nell/
SUMMARY:FDS Seminar: Jonah Botvinick-Greenhouse (Cornell)\, "Measure Transp
 ort for Data-Driven Dynamical Systems"
DESCRIPTION:\nZoom Password: 123\n\n\n\nAbstract: Learning dynamical system
 s in the presence of measurement noise\, sparse observations\, and uncerta
 inty is a central challenge in data-driven science and engineering. In thi
 s talk\, I will present new methods based on measure transport that target
  long-term statistical behavior rather than pointwise time-series reconstr
 uction\, making them well suited to regimes where standard trajectory-base
 d methods struggle. Our approach reframes system identification as a distr
 ibution-matching problem in which invariant measures extracted from data a
 re compared with synthetic invariant measures obtained as stationary solut
 ions of a Fokker–Planck equation. This leads to a PDE-constrained optimi
 zation framework that enables inference from slowly sampled data and uncer
 tainty quantification for downstream forecasting. To ensure identifiabilit
 y from the invariant measure alone\, we introduce a data-driven coordinate
  transformation inspired by Takens’ embedding theorem. While classical e
 mbedding methods assume dynamics are noise-free\, we leverage tools from o
 ptimal transport to extend these ideas to a probabilistic setting. This fo
 rmulation poses state reconstruction as a transformation between distribut
 ions\, yielding a practical computational framework for recovering high-di
 mensional systems from partial observations. We demonstrate the effectiven
 ess of our methods through numerical studies on fluid flows\, Hall-effect 
 thrusters\, and large-scale geophysical datasets.\n\n\n\nBio: Jonah Botvin
 ick-Greenhouse is a fifth-year Ph.D. student at Cornell University’s Cen
 ter for Applied Mathematics (CAM)\, advised by Yunan Yang. He received Bac
 helor’s degrees in Math and Physics from Amherst College. His research u
 ses tools from dynamical systems\, machine learning\, and optimal transpor
 t to develop principled data-driven modeling techniques for complex physic
 al systems.\n
CATEGORIES:FDS Events,Postdoctoral Applicants
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