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Advanced Modeling and Simulation in Engineering Sciences is a peer-reviewed journal published by Springer Nature, covering Model Reduction and Neural Networks, Numerical methods in engineering, Advanced Numerical Methods in Computational Mathematics.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. Advanced Modeling and Simulation in Engineering Sciences 12, 45–58 (2023).
Formats any DOI in Advanced Modeling and Simulation in Engineering Sciences style. No sign-up. |
| Publishes research in | Model Reduction and Neural Networks Numerical methods in engineering Advanced Numerical Methods in Computational Mathematics Probabilistic and Robust Engineering Design Composite Material Mechanics |
| ISSN | 2213-7467 |
| Citation impact (2-yr) | 4.24 |
| h-index | 32 |
| i10-index | 154 |
| Total citations | 4,933 |
| Article processing charge | $1,214 |
| Open access | Yes |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | amses-journal.springeropen.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract “When I use a word, it means whatever I want it to mean”: Humpty Dumpty in Alice’s Adventures Through The Looking Glass, Lewis Carroll. “Digital twin” is currently a term applied in a wide variety of ways. Some differences are variations from sector to sector, but definitions within a sector can also vary significantly.…
The goal of the present study is to illustrate the full integration of sensor and imaging data into numerical procedures for the purpose of identification of constitutive laws and their validation. The feasibility of such approaches is proven in the context of in situ tests monitored by tomography. The bridging tool consists of spatiotemporal (i.e.,…
One question in the context of immersed boundary or fictitious domain methods is how to compute discontinuous integrands in cut elements accurately. A frequently used method is to apply a composed Gaussian quadrature based on a spacetree subdivision. Although this approach works robustly on any geometry, the resulting integration mesh yields a low order representation…
Abstract In this paper, we propose a general framework for projection-based model order reduction assisted by deep neural networks. The proposed methodology, called ROM-net , consists in using deep learning techniques to adapt the reduced-order model to a stochastic input tensor whose nonparametrized variabilities strongly influence the quantities of interest for a given physics problem.…
Abstract The present work aims at the identification of the effective constitutive behavior of $$\Sigma 5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Σ</mml:mi><mml:mn>5</mml:mn></mml:mrow></mml:math> aluminum grain boundaries (GB) for proportional loading by using machine learning (ML) techniques. The input for the ML approach is high accuracy data gathered in challenging molecular dynamics (MD) simulations at the atomic scale for varying temperatures…
