Submission Deadline: 31 August 2025 View: 266 Submit to Special Issue
Prof. Dr. Víctor Leiva
Email: victor.leiva@pucv.cl
Affiliation: School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
Research Interests: Artificial Intelligence, computational modelling, data analytics, machine learning, statistical methods
Dr. Cecilia Castro
Email: cecilia@math.uminho.pt
Affiliation: Centre of Mathematics, Universidade do Minho, Braga 4710-057, Portugal
Research Interests: Artificial Intelligence, computational modelling, data analytics, machine learning, statistical methods
The integration of artificial intelligence (AI), machine learning (ML), and statistical methods has revolutionized computational modeling in engineering and applied sciences. This special issue focuses on innovative approaches combining data-driven techniques, statistical learning frameworks, and computational methods to enhance efficiency, accuracy, and adaptability in complex systems.
We invite contributions that explore advanced models for predictive analytics, process optimization, digital twin development, and uncertainty quantification. Special emphasis is placed on applications integrating data-driven and physics-based models, enabling more robust and interpretable solutions across various domains. This issue also addresses challenges such as integrating diverse data sources, ensuring model reliability, and scaling computational models.
Contributions from researchers and practitioners working at the forefront of AI, ML, statistical modeling, and applied computational methods are encouraged. Through this collaborative effort, we aim to drive innovation and promote the development of cutting-edge computational techniques for real-world applications.
Scope and Themes:
This special issue invites submissions addressing, but not limited to, the following topics:
· Machine Learning for Computational Simulations: Methods and applications of ML for enhancing simulation efficiency, accuracy, and scalability.
· Artificial Intelligence and Statistical Models in Data-Driven Computational Modeling: Integration of AI, ML, and statistical frameworks for predictive modeling, uncertainty quantification, and decision-making processes.
· Neural Networks, Deep Learning, and Data-Driven Approaches in Engineering: Applications in areas such as fluid mechanics, structural analysis, materials science, and dynamic system modeling.
· AI-Based Optimization and Statistical Design: Applying AI, ML, and statistical optimization techniques for engineering design and process improvement.
· Digital Twins, Predictive Analytics, and Statistical Forecasting: Development and application of digital twins for real-time monitoring, forecasting, and adaptive control of real-world systems.
· Challenges and Solutions in Data-Driven Computational Modeling: Issues of interpretability, model robustness, uncertainty quantification, and integration with traditional physics-based methods.
· Case Studies and Practical Applications: Real-world examples of AI, ML, and statistical models applied to engineering, scientific modeling, and decision-support systems.
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