Deadline Date: 30 June 2026
The increasingly complex engineering problems demand new, robust, and synergic methodological approaches, able to provide new and innovative solutions. This special issue is dedicated to exploring the fields of machine learning, surrogate modeling, and optimization in computational mechanics, highlighting the latest advancements in these areas and their intersections.
The wide scope of this special issue includes machine learning algorithms able to create new high-quality models as well as the establishment of advanced optimization frameworks integrating metamodels.
Specific areas of interest include but are not limited to: Machine Learning, Artificial Intelligence, Surrogate Models, Optimization, Engineering Design, Computational Mechanics, Computational Design, High-Fidelity Models, Uncertainty Quantification, Computational Fluid Dynamics, Finite Element Analysis, Data-Driven Design, AI-Accelerated Engineering, among other topics.
Keywords: Machine Learning, Surrogate Models, Optimization, Computational Mechanics, Computational Modelling and Design
Editorial board
