BEGIN:VCALENDAR
PRODID:-//Microsoft Corporation//Outlook 12.0 MIMEDIR//EN
VERSION:2.0
METHOD:PUBLISH
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VTIMEZONE
TZID:GMT Standard Time
BEGIN:STANDARD
DTSTART:16010101T020000
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=-1SU;BYMONTH=10
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:16010101T010000
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=-1SU;BYMONTH=3
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
CLASS:PUBLIC
CREATED:20091109T101015Z
DESCRIPTION:Speaker: Gevik Grigorian University College London\n\nTopic: Scientific machine learning for inferring missing components of partially known mechanistic models, with biomedical applications\n<p> <span><span>Modelling complex dynamics mechanistically can be challenging as gaps in the existing knowledge and/or difficulties obtaining certain parameter values often result in incomplete models. In such scenarios, recent advances in machine learning have demonstrated the possibility of developing partially-learned (or "grey box") models, wherein certain components of a mathematical (or "white box") model are set to be governed by a learned (or "black box") system. In our work, we replace unknown components of incomplete mathematical models with a neural network and use the available data to train the network to capture the missing dynamics of the system. This allows for a subsequent inference step, where the trained neural network embedded in the system equations is regressed down to mathematical expressions using symbolic regression. In this talk, we showcase three separate applications of this method. Firstly, we demonstrate that it is possible to learn governing equations of unobserved states in dynamical systems. Secondly, we present an alternative means of modelling ventricular interaction in a lumped parameter model of the cardiovascular system using this approach. Lastly, we show that improvements can be made to a slightly dated but widely used blood gas model. </span></span></p>
DTSTART;TZID=GMT Standard Time:20240124T12:35:00
DTEND;TZID=GMT Standard Time:20240124T13:25:00
TZID=GMT Standard Time
DTSTAMP:20100109T093305Z
LAST-MODIFIED:20091109T101015Z
LOCATION:Harrison 170
PRIORITY:5
SEQUENCE:0
SUMMARY;LANGUAGE=en-gb:Scientific machine learning for inferring missing components of partially known mechanistic models, with biomedical applications
TRANSP:OPAQUE
UID:040000008200E00074C5B7101A82E008000000008062306C6261CA01000000000000000
X-MICROSOFT-CDO-BUSYSTATUS:BUSY
X-MICROSOFT-CDO-IMPORTANCE:1
X-MICROSOFT-DISALLOW-COUNTER:FALSE
X-MS-OLK-ALLOWEXTERNCHECK:TRUE
X-MS-OLK-AUTOFILLLOCATION:FALSE
X-MS-OLK-CONFTYPE:0
BEGIN:VALARM
TRIGGER:-PT1440M
ACTION:DISPLAY
DESCRIPTION:Reminder
END:VALARM
END:VEVENT
END:VCALENDAR