BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Automated cardiovascular material model discovery - Mathias Peirli
 nck (Delft University of Technology)
DTSTART:20240603T141000Z
DTEND:20240603T143000Z
UID:TALK214519@talks.cam.ac.uk
DESCRIPTION:Quantifying the biomechanical behavior of cardiovascular tissu
 es holds immense potential to enhance our understanding of (i) smooth and 
 cardiac muscle cell responses to mechanical cues and (ii) how structural a
 nd compositional alterations within these tissues impact their overall fun
 ctional behavior. Such insights are crucial to unravel disease mechanisms 
 (e.g. atherosclerosis\, aneurysm\, myocardial infarction)\, to optimize an
 d personalize current treatment strategies (e.g. annuloplasty ring or sten
 t sizing)\, and to develop novel medical devices (e.g. artificial heart va
 lves or vascular grafts). Such studies depend critically on the accuracy o
 f the underlying constitutive models\, which govern the thermodynamically 
 consistent relationship between the tissue&rsquo\;s deformation and intern
 al stress state. Given cardiovascular tissue&rsquo\;s highly non-linear\, 
 transverse isotropic or even orthotropic mechanical behavior and a vast ev
 er-increasing library of potential constitutive models\, identifying the m
 ost accurate material model can be a challenging procedure prone to signif
 icant user bias. To resolve this subjectivity and democratize computationa
 l engineering analysis for all\, we leverage constitutive artificial neura
 l networks and machine learning to automate and democratize constitutive m
 odel discovery for these intricate materials. Based on arterial and myocar
 dial biaxial tensile and triaxial shear testing data\, our framework auton
 omously identifies the optimal material models and parameters from a libra
 ry of over 232 = 4\,294\,967\,296 possible material models. By seamlessly 
 integrating the discovered material models into a universal material subro
 utine for (in)compressible (an)isotropic tissues\, our work advances the i
 mplementation of these models into cardiovascular finite element simulatio
 ns. This not only enhances user-friendliness and robustness but also mitig
 ates the vulnerability to human error. The automated approach signifies a 
 significant stride towards a more inclusive\, user-friendly\, and accurate
  framework for cardiovascular biomechanics simulations\, ultimately contri
 buting to improved medical treatments for cardiovascular diseases.
LOCATION:Seminar Room 1\, Newton Institute
END:VEVENT
END:VCALENDAR