[HTML][HTML] Methods for enabling real-time analysis in digital twins: A literature review
This paper presents a literature review on methods for enabling real-time analysis in digital
twins, which are virtual models of physical systems. The advantages of digital twins are …
twins, which are virtual models of physical systems. The advantages of digital twins are …
Engineering empowered by physics-based and data-driven hybrid models: A methodological overview
Smart manufacturing implies creating virtual replicas of the processing operations, taking
into account the material dimension and its multi-physics transformation when forming …
into account the material dimension and its multi-physics transformation when forming …
[HTML][HTML] Methodology for the assessment of the risk of failure of metastatic vertebrae through ROM-based patient-specific simulations
The structural performance of a vertebra can be significantly undermined if it develops a
tumour, that could even lead to the vertebra's structural collapse. In cancers with a high …
tumour, that could even lead to the vertebra's structural collapse. In cancers with a high …
Learning Data-Driven Stable Corrections of Dynamical Systems—Application to the Simulation of the Top-Oil Temperature Evolution of a Power Transformer
Many engineering systems can be described by using differential models whose solutions,
generally obtained after discretization, can exhibit a noticeable deviation with respect to the …
generally obtained after discretization, can exhibit a noticeable deviation with respect to the …
Hybrid twins based on optimal transport
Nowadays data is acquiring an indisputable importance in every field including engineering.
In the past, experimental data was used to calibrate state-of-the art models. Once the model …
In the past, experimental data was used to calibrate state-of-the art models. Once the model …
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
Reduced order models (ROMs) are widely used in scientific computing to tackle high-
dimensional systems. However, traditional ROM methods may only partially capture the …
dimensional systems. However, traditional ROM methods may only partially capture the …
Adaptive model reduction of high-order solutions of compressible flows via optimal transport
RL Van Heyningen, NC Nguyen… - International Journal …, 2023 - Taylor & Francis
The solution of conservation laws with parametrised shock waves presents challenges for
both high-order numerical methods and model reduction techniques. We introduce an r …
both high-order numerical methods and model reduction techniques. We introduce an r …
Hybrid twin of RTM process at the scarce data limit
To ensure correct filling in the resin transfer molding (RTM) process, adequate numerical
models have to be developed in order to correctly capture its physics, so that this model can …
models have to be developed in order to correctly capture its physics, so that this model can …
PGD based meta modelling of a lithium-ion battery for real time prediction
A Schmid, A Pasquale, C Ellersdorfer… - Frontiers in …, 2023 - frontiersin.org
Despite the existence of computationally efficient tools, the effort for parametric
investigations is currently high in industry. In this paper, within the context of Li-Ion batteries …
investigations is currently high in industry. In this paper, within the context of Li-Ion batteries …
Parametric damage mechanics empowering structural health monitoring of 3D woven composites
This paper presents a data-driven structural health monitoring (SHM) method by the use of
so-called reduced-order models relying on an offline training/online use for unidirectional …
so-called reduced-order models relying on an offline training/online use for unidirectional …