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Data-Based Engineering and Computations

Guest Editors:

  • Elias Cueto (University of Zaragoza, Zaragoza, Spain)
  • Francisco Chinesta (PIMM Laboratory, Arts et Métiers Institute of Technology, HESAM Université, Paris, France)
  • Charbel Farhat (Stanford University, California, USA)
  • Pierre Ladeveze (Université Paris-Saclay, Gif-sur-Yvette, France)
  • Francisco Javier Montans (Universidad Politécnica de Madrid, Madrid, Spain)

Engineering is evolving in the same way as society. Nowadays, data is earning a prominence never imagined before. In the past, in the domain of materials, processes and structures, testing machines allowed the extraction of data, which served in turn to calibrate state-of-the- art computational models. 

Some calibration procedures were even integrated within testing machines. Thus, once the model was calibrated, computer simulation took place. However, data can offer much more than a simple state-of-the-art model calibration, and not only from its simple statistical analysis, but from the modeling and simulation viewpoints. 

This gives rise to the family of so-called digital twins, also known as virtual and hybrid twins. Moreover, not only data can serve to enrich physically-based models. These could allow us to perform a tremendous leap forward, by replacing big-data-based habits by the incipient smart-data paradigm. 

In this collection, we will cover recent advances in the field, with a particular emphasis on grey-box approaches, i.e., those in which the laws of physics are included in the approach.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies, including those pertaining to Collections. Articles will be added to the Collection as they are published.

  1. Materials with sufficient strength and stiffness can transfer nonlinear design loads without damage. The present study compares crack propagation speed and shape in rock-like material and sandstone when subjec...

    Authors: Omer Mughieda, Lijie Guo, Yunchao Tang, Nader M. Okasha, Sayed Javid Azimi, Abdoullah Namdar and Falak Azhar
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2024 11:4
  2. In the present work, we introduce a novel approach to enhance the precision of reduced order models by exploiting a multi-fidelity perspective and DeepONets. Reduced models provide a real-time numerical approx...

    Authors: Nicola Demo, Marco Tezzele and Gianluigi Rozza
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:12
  3. Regressions created from experimental or simulated data enable the construction of metamodels, widely used in a variety of engineering applications. Many engineering problems involve multi-parametric physics w...

    Authors: Abel Sancarlos, Victor Champaney, Elias Cueto and Francisco Chinesta
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2023 10:4
  4. The multiscale method called Pseudo-Direct Numerical Simulation (P-DNS) is presented as a Reduced Order Model (ROM) aiming to solve problems obtaining similar accuracy to a solution with many degrees of freedo...

    Authors: Sergio R. Idelsohn, Juan M. Gimenez and Norberto M. Nigro
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:22
  5. Simulation-based engineering has been a major protagonist of the technology of the last century. However, models based on well established physics fail sometimes to describe the observed reality. They often ex...

    Authors: Francisco Chinesta and Elias Cueto
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:21
  6. A nonparametric method assessing the error and variability margins in solutions depicted in a separated form using experimental results is illustrated in this work. The method assess the total variability of t...

    Authors: Chady Ghnatios and Anais Barasinski
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2021 8:20