BEGIN:VCALENDAR METHOD:REQUEST PRODID:Microsoft Exchange Server 2010 VERSION:2.0 BEGIN:VTIMEZONE TZID:Eastern Standard Time BEGIN:STANDARD DTSTART:16010101T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=1SU;BYMONTH=11 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010101T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=2SU;BYMONTH=3 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT ORGANIZER;CN=Michael McDonald:mailto:michael.mcdonald@fsu.edu DESCRIPTION;LANGUAGE=en-US:"Nested Operator Inference for Multifidelity Unc ertainty Quantification in Ice Sheet Simulations"\n\nNicole Aretz\nUnivers ity of Texas at Austin\n\nPlease feel free to forward/share this invitatio n with other groups/disciplines that might be interested in this talk/topi c. All are welcome to attend.\n\nNOTE: In-person attendance is requested i n our 499 Dirac Science Library (DSL) Seminar Room. Zoom access is intende d for external (non-departmental) participants only.\n\nhttps://fsu.zoom.u s/j/94273595552\nMeeting # 942 7359 5552\n\n🎦 Colloquium recordings wil l be made available here\, https://www.sc.fsu.edu/colloquium\n\n\nFRIDAY\, Ja n 16\, 2026\, Schedule:\n\n* 3:30 to 4:30 PM Eastern Time (US and Canada)\ n🕟 Colloquium - 499 DSL Seminar Room\n\n\nAbstract:\nWe present a neste d Operator Inference method for data-driven learning of physics-based redu ced-order models. The goal of the approach is to approximate the solution of highly accurate but computationally expensive “full-order” models\, for example to enable multifidelity uncertainty quantification.\n\nProjec tion-based model order reduction methods exploit the intrinsic low-dimensi onality of the full-order solution manifold. These reduced-order models (R OMs) typically achieve significant computational savings while remaining p hysically interpretable through the governing equations. Operator Inferenc e (OpInf) is a data-driven learning technique to construct projection-base d ROMs without accessing the full-order operators. Because the degrees of freedom in the classic OpInf learning problem scale polynomially in the di mension of the reduced space\, classic OpInf requires precise regularizati on to balance the numerical stability of the OpInf learning problem\, the structural stability of the learned ROM\, and the achieved reconstruction accuracy. Nested OpInf exploits the inherent hierarchy within the reduced space to iteratively construct initial guesses for the OpInf learning prob lem that prioritize the interactions of the dominant modes. The initial gu ess computed for any target reduced dimension corresponds to a ROM with pr ovably smaller or equal snapshot reconstruction error than with standard O pInf. Driven by the need of multifidelity uncertainty quantification metho ds for different types of surrogate models\, we show how to use nested OpI nf to build a ROM of the Greenland ice sheet. Under moderate climate forci ng\, the learned ROM achieves four orders of magnitude in computational sp eed-up while keeping the generalization error for unseen parameters below 5% over a 30 year simulation horizon.\n\n\nAdditional colloquium details c an be found here\,\nhttps://www.sc.fsu.edu/news-and-events/colloquium/1900 -colloquium-with-nicole-aretz-2026-01-16\n\n__________________________________________ ______________________________________\nMicrosoft Teams Need help?\nJoin the meeting now\nMeeting ID: 223 041 900 5 33 60\nPasscode: dX3MF3vG\n________________________________\nDial in by ph one\n+1 850-702-3768\,\,329647645# United States\, Tallahassee\nFind a local number\nPhone conference ID : 329 647 645#\nFor organizers: Meeting options | Reset dial-in PIN \nOrg help\n____________________________________________________________ ____________________\n UID:040000008200E00074C5B7101A82E00800000000C1FBA4825D85DC01000000000000000 010000000F93D97391944E84993DD77E74800F4E9 SUMMARY;LANGUAGE=en-US:Scientific Computing Colloquium with Nicole Aretz DTSTART;TZID=Eastern Standard Time:20260116T153000 DTEND;TZID=Eastern Standard Time:20260116T162500 CLASS:PUBLIC PRIORITY:5 DTSTAMP:20260114T135905Z TRANSP:OPAQUE STATUS:CONFIRMED SEQUENCE:0 LOCATION;LANGUAGE=en-US:https://fsu.zoom.us/j/94273595552 X-MICROSOFT-CDO-APPT-SEQUENCE:0 X-MICROSOFT-CDO-OWNERAPPTID:2124509377 X-MICROSOFT-CDO-BUSYSTATUS:TENTATIVE X-MICROSOFT-CDO-INTENDEDSTATUS:BUSY X-MICROSOFT-CDO-ALLDAYEVENT:FALSE X-MICROSOFT-CDO-IMPORTANCE:1 X-MICROSOFT-CDO-INSTTYPE:0 X-MICROSOFT-ONLINEMEETINGINFORMATION:{"OnlineMeetingChannelId":null\,"Onlin eMeetingProvider":3} X-MICROSOFT-ONLINEMEETINGTOLLNUMBER:+1 850-702-3768 X-MICROSOFT-ONLINEMEETINGCONFERENCEID:329647645 X-MICROSOFT-SKYPETEAMSMEETINGURL:https://teams.microsoft.com/l/meetup-join/ 19%3ameeting_OTEwMzNhYmEtNWEwYy00Njc5LWEzNjAtNmRhMjk0NDA3YmM5%40thread.v2/ 0?context=%7b%22Tid%22%3a%22a36450eb-db06-42a7-8d1b-026719f701e3%22%2c%22O id%22%3a%22066cc030-685a-4332-a595-fc2d5399ad89%22%7d X-MICROSOFT-SCHEDULINGSERVICEUPDATEURL:https://api.scheduler.teams.microsof t.com/teams/a36450eb-db06-42a7-8d1b-026719f701e3/066cc030-685a-4332-a595-f c2d5399ad89/19_meeting_OTEwMzNhYmEtNWEwYy00Njc5LWEzNjAtNmRhMjk0NDA3YmM5@th read.v2/0 X-MICROSOFT-SKYPETEAMSPROPERTIES:{"cid":"19:meeting_OTEwMzNhYmEtNWEwYy00Njc 5LWEzNjAtNmRhMjk0NDA3YmM5@thread.v2"\,"rid":0\,"mid":0\,"uid":null\,"priva te":true\,"type":0} X-MICROSOFT-DONOTFORWARDMEETING:FALSE X-MICROSOFT-DISALLOW-COUNTER:FALSE X-MICROSOFT-REQUESTEDATTENDANCEMODE:DEFAULT X-MICROSOFT-ISRESPONSEREQUESTED:FALSE X-MICROSOFT-LOCATIONDISPLAYNAME:https://fsu.zoom.us/j/94273595552 X-MICROSOFT-LOCATIONSOURCE:None X-MICROSOFT-LOCATIONS:[{"DisplayName":"https://fsu.zoom.us/j/94273595552"\, "LocationAnnotation":""\,"LocationUri":""\,"LocationStreet":""\,"LocationC ity":""\,"LocationState":""\,"LocationCountry":""\,"LocationPostalCode":"" \,"LocationFullAddress":""}] BEGIN:VALARM DESCRIPTION:REMINDER TRIGGER;RELATED=START:-PT5M ACTION:DISPLAY END:VALARM END:VEVENT END:VCALENDAR