Digital Twinning Organoids, Engineering and Environmental Systems
Presenter
DescriptionWe present recent advances in the field of digital twinning of environmental, engineering and biological systems.
We discuss recently developed methods for model order reduction, data assimilation, machine learning of severely non-linear and time-dependent problems. We also discuss parameter identification and uncertainty quantification for elasto-plasticity, turbulent flow and coupled sets of parabolic differential equations. The methods we use include convolutional neural networks, Kalman filters, proper orthogonal decomposition, clustering of modes and Bayesian neural nets.
The applications we discuss include urban comfort and wind energy harvesting, morphology impact on astrocyte metabolism, digital twins of keloids, chemical vapor deposition, cancer growth and surgical simulation/planning/training.
We also discuss open-source implementation issues including the FEniCS, ACEGEN/FEM and SOFA frameworks.
https://www.sciencedirect.com/science/article/abs/pii/S0045794921001425
https://arxiv.org/pdf/2111.01867
https://www.sciencedirect.com/science/article/am/pii/S0307904X19304755
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/C1B47742890EA01AC0531659FDFC384F/S2632673621000095a.pdf/bayesian_model_uncertainty_quantification_for_hyperelastic_soft_tissue_models.pdf
https://link.springer.com/article/10.1007/s00466-021-02112-3
https://link.springer.com/article/10.1007/s00366-021-01597-z
We discuss recently developed methods for model order reduction, data assimilation, machine learning of severely non-linear and time-dependent problems. We also discuss parameter identification and uncertainty quantification for elasto-plasticity, turbulent flow and coupled sets of parabolic differential equations. The methods we use include convolutional neural networks, Kalman filters, proper orthogonal decomposition, clustering of modes and Bayesian neural nets.
The applications we discuss include urban comfort and wind energy harvesting, morphology impact on astrocyte metabolism, digital twins of keloids, chemical vapor deposition, cancer growth and surgical simulation/planning/training.
We also discuss open-source implementation issues including the FEniCS, ACEGEN/FEM and SOFA frameworks.
https://www.sciencedirect.com/science/article/abs/pii/S0045794921001425
https://arxiv.org/pdf/2111.01867
https://www.sciencedirect.com/science/article/am/pii/S0307904X19304755
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/C1B47742890EA01AC0531659FDFC384F/S2632673621000095a.pdf/bayesian_model_uncertainty_quantification_for_hyperelastic_soft_tissue_models.pdf
https://link.springer.com/article/10.1007/s00466-021-02112-3
https://link.springer.com/article/10.1007/s00366-021-01597-z
TimeMonday, June 2717:00 - 17:30 CEST
LocationBoston 3 Room
SessionMS2H - HPC in Reduced Order Modelling for Advanced Mechanics Simulations and Digital Twinning
Session Chair
Event Type
Minisymposium
Chemistry and Materials
Computer Science and Applied Mathematics
Engineering