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ORGANIZER;CN=Michael McDonald:mailto:michael.mcdonald@fsu.edu
DESCRIPTION;LANGUAGE=en-US:"Conservative cell-average-based neural network 
 method for nonlinear conservation laws"\n\nJue Yan\nDepartment of Mathemat
 ics\nIowa State University\n\nPlease feel free to forward/share this invit
 ation with other groups/disciplines that might be interested in this talk/
 topic. All are welcome to attend.\n\nNOTE: In-person attendance is request
 ed in our 499 Dirac Science Library (DSL) Seminar Room. Zoom access is int
 ended for external (non-departmental) participants only.\n\n\nhttps://fsu.
 zoom.us/j/94273595552\nMeeting # 942 7359 5552\n\n\n🎦 Colloquium record
 ings will be made available here\, https://www.sc.fsu.edu/colloquium<https
 ://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.ed
 u%2Fcolloquium&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7C0c3f58
 b9813b41e3654208de8db71c26%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C63
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 data=oYM6oMy4brNJogzu2jINB6UNBsfIscb0uoOl4%2FkehG4%3D&reserved=0>\n\n\nWed
 nesday\, Apr 1\, 2026\, Schedule:\n\n* 3:00 to 3:30 PM Eastern Time (US an
 d Canada)\n☕ Nespresso & Teatime - 417 DSL Commons\n\n* 3:30 to 4:30 PM 
 Eastern Time (US and Canada)\n🕟 Colloquium - 499 DSL Seminar Room\n\n\n
 Abstract:\nIn this talk\, we present the recently developed Cell-Average-B
 ased Neural Network (CANN) for time-dependent PDEs. Spatial and temporal d
 iscretization in conventional methods is replaced by training a simple fee
 dforward network to obtain an explicit one-step finite volume method. The 
 well-trained network parameters function as the scheme coefficients. The n
 etwork method has a minimum number of unknowns\, which can be quickly trai
 ned on a small training dataset. Unlike conventional numerical methods\, t
 he CANN approach is not limited by the CFL conditions\, enabling the use o
 f significantly larger time steps. This leads to a highly efficient comput
 ational method for solving PDEs. The conservative version of the CANN meth
 od for nonlinear conservation laws will be discussed. The method\, trained
  on the smooth-solution data from ONE initial-value problem\, is verified 
 to solve any initial-value problem involving shocks and rarefaction waves.
  The network can accurately learn a numerical flux\, such as the Lax-Fried
 richs flux\, thereby guaranteeing convergence to the physically relevant e
 ntropy solutions. The extension of the method to non-uniform meshes will b
 e considered. A bound-preserving version of the network method is presente
 d for linear Advection equations.\n\n\nAdditional colloquium details can b
 e found here.\nhttps://www.sc.fsu.edu/news-and-events/colloquium/1915-coll
 oquium-with-jue-yan-2026-04-01<https://nam04.safelinks.protection.outlook.
 com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fnews-and-events%2Fcolloquium%2F191
 5-colloquium-with-jue-yan-2026-04-01&data=05%7C02%7Csc-seminar-announce%40
 lists.fsu.edu%7C0c3f58b9813b41e3654208de8db71c26%7Ca36450ebdb0642a78d1b026
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SUMMARY;LANGUAGE=en-US:Scientific Computing Colloquium with Jue Yan
DTSTART;TZID=Eastern Standard Time:20260401T153000
DTEND;TZID=Eastern Standard Time:20260401T162500
CLASS:PUBLIC
PRIORITY:5
DTSTAMP:20260329T171752Z
TRANSP:OPAQUE
STATUS:CONFIRMED
SEQUENCE:0
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