期刊信息

Advanced Modeling and Simulation in Engineering Sciences

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影响因子:
3.2
出版商:
Springer
ISSN:
2213-7467
浏览:
17070
关注:
1

征稿

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.
由 Dou Sun 最后更新于

Special Issues

Special Issue on Innovations in Computational Mechanics and Machine Learning Driven Solutions 截稿日期: 2026-07-15 Deep learning tools in computational mechanics hold significant importance in current scientific and engineering research. The integration of both data-driven disciplines and computational mechanics is transforming the research landscape, influencing how we design, simulate, analyze and optimize complex systems. Merging knowledge coming from these two fields provides new insights for a deeper understanding of mechanics. These synergies appear in multiple industrial applications, from aerospace to material science and civil infrastructures, becoming a critical, and interdisciplinary, area of study. Although one of the most active fields is the so-called physics-informed machine learning, the use of these techniques with statistical foundation has had a great impact in other studies such as to learn patterns in massive datasets and unveil correlations in the available data. The final goal is always to highlight new insights, which improve the scientific understanding of mechanics, while facilitating optimization through surrogates. Moreover, inverse modeling becomes at hand through reducing the computational cost. This Collection focuses on the latest advances in machine learning and deep learning for computational mechanics applications. The goal is to showcase recent advances in the development and understanding of coupled machine learning and physical modeling for complex physical systems, along with their applications across industrial domains. Submissions aligned with the following topics are expected: Advances of simulation for predicting complex behaviors of materials, structures, and dynamics Real time simulation and control in multiscale analysis Model order reduction coupled with machine learning applications Model calibration, adaptation, and correction Stochastic and uncertainty analysis in complex systems and data assimilation 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 "Innovations in Computational Mechanics and Machine Learning Driven Solutions" 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.
由 Dou Sun 最后更新于
Special Issue on Advanced Modeling and Simulation of Composite Materials: Physics-Based and Data-Driven Approaches 截稿日期: 2026-07-15 Advancements in composite materials are transforming key industries such as aerospace, automotive, and civil engineering, thanks to their exceptional mechanical properties and lightweight nature. As the demand for high-performance, sustainable materials grows, so does the need for innovative modeling and simulation techniques that can accurately predict behavior under diverse conditions. This Collection focuses on the convergence of physics-based approaches and data-driven methods, including machine learning, to enhance our understanding and design of composite materials. Recent developments have enabled more precise failure prediction, optimization of manufacturing processes, and the creation of smart composites capable of adapting to environmental changes. Hybrid modeling techniques which combine experimental data with computational simulations are paving the way for breakthroughs in material design and performance. We invite contributions that explore novel methodologies, interdisciplinary approaches, and practical applications aimed at improving the reliability, efficiency, and sustainability of composite materials. By fostering collaboration across domains, this Collection seeks to push the boundaries of current knowledge and support the development of next-generation engineering solutions. 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 "Advanced Modeling and Simulation of Composite Materials: Physics-Based and Data-Driven Approaches" 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.
由 Dou Sun 最后更新于
Special Issue on Scientific Machine Learning for Risk and Vulnerability Analysis in Structural Systems 截稿日期: 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.
由 Dou Sun 最后更新于

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