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BEGIN:VEVENT
DTSTAMP:20220812T074334Z
LOCATION:Boston 3 Room
DTSTART;TZID=Europe/Stockholm:20220627T170000
DTEND;TZID=Europe/Stockholm:20220627T173000
UID:submissions.pasc-conference.org_PASC22_sess138_msa229@linklings.com
SUMMARY:Digital Twinning Organoids, Engineering and Environmental Systems
DESCRIPTION:Minisymposium\n\nDigital Twinning Organoids, Engineering and E
 nvironmental Systems\n\nBordas, Skupin, Farina, Urcun, Koronaki...\n\nWe p
 resent recent advances in the field of digital twinning of environmental, 
 engineering and biological systems. <br /><br />We discuss recently develo
 ped methods for model order reduction, data assimilation, machine learning
  of severely non-linear and time-dependent problems. We also discuss param
 eter identification and uncertainty quantification for elasto-plasticity, 
 turbulent flow and coupled sets of parabolic differential equations. The m
 ethods we use include convolutional neural networks, Kalman filters, prope
 r orthogonal decomposition, clustering of modes and Bayesian neural nets.<
 br /><br />The applications we discuss include urban comfort and wind ener
 gy harvesting, morphology impact on astrocyte metabolism, digital twins of
  keloids, chemical vapor deposition, cancer growth and surgical simulation
 /planning/training. <br /><br />We also discuss open-source implementation
  issues including the FEniCS, ACEGEN/FEM and SOFA frameworks.<br /><br />h
 ttps://www.sciencedirect.com/science/article/abs/pii/S0045794921001425<br 
 />https://arxiv.org/pdf/2111.01867 <br />https://www.sciencedirect.com/sci
 ence/article/am/pii/S0307904X19304755<br />https://www.cambridge.org/core/
 services/aop-cambridge-core/content/view/C1B47742890EA01AC0531659FDFC384F/
 S2632673621000095a.pdf/bayesian_model_uncertainty_quantification_for_hyper
 elastic_soft_tissue_models.pdf<br />https://link.springer.com/article/10.1
 007/s00466-021-02112-3<br />https://link.springer.com/article/10.1007/s003
 66-021-01597-z\n\nDomain: Chemistry and Materials, Computer Science and Ap
 plied Mathematics, Engineering
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