Session

DescriptionComputational mechanics simulations offer a valuable analysis tool for engineering applications ranging from design and conceptualization all the way to the operation and maintenance of engineered systems. Such simulations often involve very detailed High-Fidelity Models (HFM) accounting for complex physics, such as failure, nonlinearities and time-dependence, as well as multi-physics interactions. The resulting computational complexity can be prohibitive for several applications. This is particularly true for Digital Twinning applications, where the digital representation is often coupled with monitoring data derived from the physical counterpart of the Twin. In this case, models have to be evaluated repeatedly for reliably updating the digital representation, in certain cases even in real-time. Reduced Order Modelling (ROM) techniques allow to substantially reduce the involved computational cost, while preserving precision. These involve an offline stage, where a ROM is constructed based on information from the HFM, and an online stage where the ROM is deployed. The exploitation of HPC infrastructure is indispensable in both the offline and online phase, for ensuring robust training and real-time estimation capabilities, respectively. In this MS, recent advances in ROM methods, algorithms, and application challenges will be discussed, with a particular focus on exploitation of HPC capabilities and assimilation with data.