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SUMMARY:QLS Seminar - Physics-Informed Machine Learning for Biological Sys
 tems
DTSTART;VALUE=DATE-TIME:20260715T120000Z
DTEND;VALUE=DATE-TIME:20260715T130000Z
DTSTAMP;VALUE=DATE-TIME:20260717T142041Z
UID:indico-event-11403@ictp.it
DESCRIPTION:\n	Physics-informed machine learning (PIML) is emerging as a p
 owerful framework for modeling complex biological systems by integrating m
 echanistic knowledge with experimental data. Unlike purely data-driven app
 roaches\, PIML incorporates differential equations describing biological p
 rocesses while enabling unknown mechanisms to be learned directly from spa
 rse\, noisy\, and partially observed measurements. This capability is part
 icularly valuable in biology\, where mechanistic models are often incomple
 te and experimental data are limited. In this talk\, I will present recent
  advances from our group in developing interpretable physics-informed lear
 ning methods for biomedical applications. I will introduce Universal Physi
 cs-Informed Neural Networks (UPINNs)\, which extend conventional PINNs by 
 simultaneously estimating unknown parameters and discovering previously un
 known biological functions from data. I will demonstrate applications in q
 uantitative systems pharmacology\, drug development\, and cancer biology\,
  and discuss how these approaches can accelerate mechanistic discovery\, i
 mprove predictive modeling\, and support the development of next-generatio
 n biological digital twins.\n\n//indico.ictp.it/event/11403/
LOCATION:ICTP Leonardo Building - Luigi Stasi Seminar Room
URL://indico.ictp.it/event/11403/
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