Información de la Revista

Advanced Modeling and Simulation in Engineering Sciences

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Factor de Impacto:
3.2
Editor:
Springer
ISSN:
2213-7467
Vistas:
17441
Seguidores:
1

Solicitud de Artículos

Advanced Modeling and Simulation in Engineering Sciences is an academic journal published by Springer. (ISSN 2213-7467, impact factor 3.2).

Aims and scope The research topics addressed by Advanced Modeling and Simulation in Engineering Sciences (AMOS) cover the vast domain of the advanced modeling and simulation of materials, processes and structures governed by the laws of mechanics. The emphasis is on advanced and innovative modeling approaches and numerical strategies. The main objective is to describe the actual physics of large mechanical systems with complicated geometries as accurately as possible using complex, highly nonlinear and coupled multiphysics and multiscale models, and then to carry out simulations with these complex models as rapidly as possible. In other words, this research revolves around efficient numerical modeling along with model verification and validation. Therefore, the corresponding papers deal with advanced modeling and simulation, efficient optimization, inverse analysis, data-driven computation and simulation-based control. These challenging issues require multidisciplinary efforts – particularly in modeling, numerical analysis and computer science – which are treated in this journal.
Última actualización por Dou Sun el

Special Issues

Special Issue on Scientific Machine Learning for Risk and Vulnerability Analysis in Structural Systems Día de Entrega: 2026-08-25 Structural systems are increasingly exposed to complex risks and vulnerabilities due to natural hazards, aging infrastructure, and evolving design requirements. Scientific Machine Learning (SciML) offers transformative potential in addressing these challenges by integrating physics-based models with data-driven approaches. This collection aims to bring together cutting-edge research that leverages SciML for risk analysis, vulnerability assessment, and uncertainty quantification in structural engineering. Contributions will explore innovative methodologies, computational frameworks, and applications that enhance resilience and reliability in structural systems. Submissions aligned with the following topics are expected: Scientific Machine Learning for structural engineering applications Risk analysis and vulnerability assessment of structural systems Physics-informed machine learning models for structural reliability Digital twins for predictive maintenance and risk mitigation Hybrid modeling approaches combining physics-based and data-driven techniques Advanced computational methods for resilience and safety evaluation Submission guidelines All papers must be prepared in accordance with the Submission Guidelines. Articles for this Special Issue should be submitted via our submission system, SNAPP. During the submission process you will be asked whether you are submitting to a Collection, please select "Scientific Machine Learning for Risk and Vulnerability Analysis in Structural Systems" from the dropdown menu. Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation by at least two reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Final decisions on all papers are made by the Editor-in-Chief.
Última actualización por Dou Sun el

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