
Academic Journal
Q1Advanced Modeling and Simulation in Engineering Sciences
About Advanced Modeling and Simulation in Engineering Sciences
Advanced Modeling and Simulation in Engineering Sciences is a scholarly journal published by SpringerOpen. SCImago 2025 lists it in Q1, with an SJR of 0.792 and H-index of 29.
Coverage: 2014-2026. Research categories: Applied Mathematics (Q1); Engineering (miscellaneous) (Q1); Modeling and Simulation (Q1); Computer Science Applications (Q2).
Open-access policies and author information
Reported in the official DOAJ public CSV snapshot (2026-09-01), downloaded 2026-10-03. Record updated 2026-03-17. This snapshot does not establish today’s listing status or fee quotation.
Publisher policy links recorded by DOAJ
- Aims and scope ↗
- Editorial board ↗
- Instructions for authors ↗
- Peer-review policy ↗
- Publication fees ↗
- Fee waivers ↗
- Licence terms ↗
- Copyright policy ↗
- Preservation policy ↗
Source: DOAJ journal record. Journal metadata is distributed by DOAJ under CC0. Confirm current fees, tax, eligibility and waiver terms with the publisher.
Source-backed journal facts
Topics in published research
Model Reduction and Neural Networks; Numerical methods in engineering; Advanced Numerical Methods in Computational Mathematics; Probabilistic and Robust Engineering Design; Composite Material Mechanics; Elasticity and Material Modeling.
OpenAlex classifies topics from published works. These topics are not the publisher’s official aims and scope.
Source: OpenAlex source record. Retrieved 2026-10-03. Source record updated 2026-10-02. OpenAlex metrics are different from SCImago metrics and the Clarivate Journal Impact Factor.
Journal Metrics
Quartile, SJR and the listed SCImago H-index use the 2025 imported SCImago dataset. A quartile may vary by subject category. Values without a source or reporting year are unverified historical entries. Verify the current Journal Impact Factor with Clarivate or the publisher before using it.
Aims & Scope
The publisher’s official aims and scope have not yet been verified for this profile. Use the journal website to check subject fit and accepted article types before submitting.
Recent Research Articles
Latest publications matched automatically by ISSN.
Generative surrogate modelling for the efficient design optimization of an RF component
Amit Kumar, Clément Mailhé, Jad Mounayer, Dominique Baillargeat et al.
2026-09-23 · DOI: 10.1186/s40323-026-00340-xA robust population mean estimation method in stratified sampling using auxiliary attributes under real and simulated populations
Mukesh Kumar Verma, Saif Ali Khan
2026-09-21 · DOI: 10.1186/s40323-026-00339-4A black box variational inference scheme for inverse problems with demanding physics-based models
Gil Robalo Rei, Christoph P. Schmidt, Jonas Nitzler, Maximilian Dinkel et al.
2026-09-05 · DOI: 10.1186/s40323-026-00335-8Foam property prediction and inception via a hybrid machine learning and model order reduction framework
Chady Ghnatios, Ilige Hage, Jose Enrico Q. Quinsaat, Daniel J. van de Pas et al.
2026-08-10 · DOI: 10.1186/s40323-026-00338-5Two-stage ROM: a hybrid reduced order model for offline and online efficiency
Rahul Dhopeshwar, Harshit Bansal, Karen Veroy
2026-08-06 · DOI: 10.1186/s40323-026-00322-zA compact microwave CSRR resonator for non-invasive glucose sensing enhanced by machine learning
Dalia N. Elsheakh, Esraa Sallam, Ibrahim H. El-Shal, Omar M. Fahmy et al.
2026-07-10 · DOI: 10.1186/s40323-026-00327-8Multiphysics modelling and interface optimization of PEDOT:PSS OECTs for stable and sensitive biosensing
Vijay Kumar Lamba, Sankit Kassa, Deepika Lamba, Aditi Kalsh et al.
2026-07-07 · DOI: 10.1186/s40323-026-00336-7Development of a digital twin of a powertrain for efficiency estimations with machine learning and physical modeling
Lennart Kopp, Niels Ockert, Luca Cadau, Markus Kley et al.
2026-06-27 · DOI: 10.1186/s40323-026-00333-wArtificial intelligence-driven physics-informed feature engineering for multiaxial fatigue life prediction of metallic materials with Bayesian optimization
Ebrahim Seidi, Farnaz Kaviari
2026-06-24 · DOI: 10.1186/s40323-026-00334-9A comprehensive study on estimating the primary crack spacing of flexural reinforced concrete components using machine learning techniques
Ahed Habib, Maan Habib, M. Talha Junaid, Salah Altoubat et al.
2026-06-24 · DOI: 10.1186/s40323-026-00330-zTransient structural analysis with proper generalized decomposition
Tomohiro Ishida, Hiroshi Ito, Kenjiro Terada
2026-06-19 · DOI: 10.1186/s40323-026-00328-7Remaining useful life of power transformers using efficient surrogates and deep learning techniques
Lila Achour, Sebastian Rodriguez, Paul-Henri Langlois, Fikri Hafid et al.
2026-06-13 · DOI: 10.1186/s40323-026-00329-6Hybrid physics: data framework for real-time forecasting of atmospheric pollutants
Amine Ammar, Francisco Chinesta
2026-06-03 · DOI: 10.1186/s40323-026-00331-yA new framework for generative design, real-time prediction, and inverse design optimization: application to microstructure
Mohammed El Fallaki Idrissi, Ismael Ben-Yelun, Jad Mounayer, Sebastian Rodriguez et al.
2026-05-29 · DOI: 10.1186/s40323-026-00326-9A tail-adjusted reliability index for non-Gaussian structural systems
Moussa Leblouba, Samer Barakat, Raghad Awad, Ahed Habib et al.
2026-05-19 · DOI: 10.1186/s40323-026-00321-0Structural natural frequency-embedded Fourier feature physics-informed neural networks for earthquake dynamic response prediction
Xiaxing Wang, Ke Du, Zepeng Li, Huan Luo et al.
2026-05-19 · DOI: 10.1186/s40323-026-00324-xTruss structure optimization via hierarchical tree search
Arvan Sedighzadeh, Matteo Torzoni, Alberto Corigliano
2026-05-12 · DOI: 10.1186/s40323-026-00320-1A methodology for the automated estimation of footprint-derived seismic behaviour modifiers in building exposure assessment
Miguel Ureña-Pliego, Javier Rodríguez-Saiz, Gonzalo Núñez-Álvarez, Miguel Marchamalo-Sacristán et al.
2026-05-11 · DOI: 10.1186/s40323-026-00323-yAn extension of the modified constitutive relation error concept for the robust multi-physics offshore wind turbine model calibration from in situ data
Antoine Roussel, Ludovic Chamoin, Jean-Philippe Argaud, Pierre Bousseau et al.
2026-04-29 · DOI: 10.1186/s40323-026-00319-8Predicting rolling shear failure of cross-laminated panels using a multiscale domain decomposition approach
J. Fernández, K. Saavedra, O. Allix, P. Gosselet et al.
2026-02-18 · DOI: 10.1186/s40323-026-00318-9Reviews
Community Reviews
Version History
October 2, 2026 at 8:58 pm
October 2, 2026