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SUMMARY:Quantitative model inference for living matter - Jörn Dunkel (MIT
 ) 
DTSTART:20240326T130000Z
DTEND:20240326T140000Z
UID:TALK213553@talks.cam.ac.uk
CONTACT:Michael te Vrugt
DESCRIPTION:Recent advances in live-imaging techniques provide unprecedent
 ed dynamical data ranging from the cellular to the organism scale. Notwith
 standing such experimental progress\, quantitative theoretical models rema
 in lacking\, even for moderately complex classes of systems. Here\, I will
  summarize our ongoing efforts to implement computational frameworks for i
 nferring predictive ordinary differential equations (ODEs)\, stochastic di
 fferential equations (SDEs)\, and partial differential equations (PDEs) fr
 om multi-scale imaging data for biological systems. As specific examples\,
  we will consider models for cell growth and division\, neural dynamics\, 
 mosquito flight behavior\, and collective animal swarming.\n
LOCATION:Center for Mathematical Sciences\, Lecture room MR4
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