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DTSTAMP:20260314T200719Z
DESCRIPTION:Controlling Transmission Neural Networks\n\nShuang Gao\, Polyte
 chnique Montréal\n	Tuesday April 7\, 12-1pm\n	Zoom Link: https://mcgill.zoom
 .us/j/87078928687\n	In Person: 550 Sherbrooke\, Room 189\n	\n	Abstract: Trans
 mission Neural Networks (TransNNs)\, introduced by Gao and Caines (2022)\,
  establish a connection between virus spreading models over networks and n
 eural networks. This talk will provide an introduction to TransNNs and pre
 sent the approximation technique in connection with the corresponding Mark
 ovian Susceptible-Infected-Susceptible (SIS) model with 2^n states\, where
  n is the number of nodes in the network. Under technical assumptions\, th
 e conditional probability of infection in the Markovian 2^n-state SIS epid
 emic model is obtained explicitly\, enabling the control via dynamic progr
 amming within the framework of Markov Decision Processes (MDP). An approxi
 mate receding horizon control method is proposed\, which offers significan
 t computational savings compared to the dynamic programming solution to MD
 P with 2^n states\, while also yielding less conservative control actions 
 compared to TransNN-based optimal control. Finally\, numerical comparisons
  will be presented among three approaches: (a) dynamic programming solutio
 ns for the MDP model\, (b) TransNN-based approximate optimal control\, and
  (c) TransNN-based receding horizon control. This is joint work with Peter
  E. Caines.\n
DTSTART:20260407T160000Z
DTEND:20260407T170000Z
SUMMARY:QLS Seminar Series - Shuang Gao
URL:/qls/channels/event/qls-seminar-series-shuang-gao-
 371935
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