The usual design approaches employed in industry are based on experimental trial-and-error procedures. As a consequence, the design process demands a large amount of resources (time, material and equipment), and heavily relies on the experience of the human resources involved. This framework has a considerable impact on the cost and time-to-market of new products. With the purpose of increasing the production efficiency, the use of numerical codes/optimization methodologies along with data-driven methods increased significantly over the last 5 years, which was additionally motivated by the development of better and faster computers that allow resorting to more realistic models. This symposium aims to bring together scientists and engineers working in different areas of optimization and data-driven models, covering all of its aspects, with focus in mechanical engineering. The topics of the Minisymposium include but are not limited to:

• Artificial Intelligence

• Machine Learning

• Neural Networks

• Genetic Programming

• Evolutionary Algorithms

• Manufacturing and additive manufacturing

• Energy

• Mechanics

• Nanotechnology

• Composites

With the development of new computational methods, optimization, design and control problems that could not be solved before or were solved using various assumptions have now become solvable with artificial intelligence algorithms. New optimization approaches and applications of existing methods on engineering problems are developing subjects. The aim of this mini-symposium is to bring academicians working on computational optimization, design and control together in order to present and discuss their researches. This mini-symposium will focus on intelligent algorithms and methods, new trends, and recent developments in computational optimization, design and control including applications in science, engineering and industry.

Topics include (but not limited to):

· Computational optimization

· Engineering optimization

· Bio-inspired algorithms

· Metaheuristics

· Simulation-driven design and optimization

· Surrogate- and knowledge-based optimization

· Scheduling

· Network optimization

· Multi-objective optimization

· Optimum control

· Active and passive control

· Earthquake resistance design

· Artificial intelligence models in design and optimization

· Artificial neural networks

The solution of complex optimization problems and other challenging tasks in engineering is becoming increasingly difficult to tackle with traditional mathematical methods. It is then of particularly high importance to look for innovative alternatives such as modern optimization algorithms and soft computing strategies. That is especially after the tools that allow their development and application are gradually maturing and becoming much more accessible and reliable.

This Minisymposium aims to bring together scientists and engineers working in different areas of optimization and soft computing methods, providing a common ground in which to share and discuss their most recent ideas and discoveries.  The topics covered range, from all sorts of optimization strategies and soft computing techniques applied to the engineering domain, including but not limited to:

• Optimization Algorithms
• Evolutionary Algorithms
• Genetic Programming
• Evolutionary Computation
• Swarm Intelligence
• Artificial Intelligence
• Machine Learning
• Neural Networks
• Support Vector Machines
• Fuzzy Systems
• Fuzzy Set Theory
• Fuzzy Optimization
• Chaos Theory and Chaotic Systems

In recent years, the production of huge amount of data in computational sciences has made attractive the capability to exploit such data to extract knowledge and enhance the prediction level. In aerodynamics, parametric studies, trade-off analyses and optimizations represent a precious information tank which could foster the usage of data-driven and data-fusion models in engineering practice. However, the maturity level of such models is quite low and the associated best practice is still in the preliminary stage: on one hand, machine learning techniques and neural networks are well-known and offer a wide range of choice for different purposes, from cluster analysis and dimensionality reduction to classification and regression; on the other hand, the type and preparation of aerodynamic/geometric data to be handled is not straightforward and may strongly depend on the real scope of the task, giving rise to widely different interpretations and forms of the data-driven application. Machine learning techniques commonly used in the area of Artificial Intelligence (AI) and Data Mining (DM) can represent a valuable support to reduce the computational cost required for aerodynamic analysis and uncertainty quantification.

This minisymposium aims at collecting and disseminating new ideas in application of machine learning and data-driven approaches for aerodynamic analysis and uncertainty quantification focusing on real world problems. This minisymposium also aims to disseminate the main activities and results of the GARTEUR action group AD/AG60 on this topic.

A special session on Optimization methods and applications in Structural Engineering will be organized under the framework of the 15th International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control to be held in Chania, Crete, Greece on 1-3 June 2023.

The main goal of this mini symposium is to present the state-of-the-art on optimization methods in structural engineering in its broad sense. This includes, but not limited to, structures subjected to static, dynamic, and temperature loadings, as well as fluid-structure interaction; size, geometry and topology optimization; deterministic, stochastic, and robust optimization, etc.

We particularly encourage presentations that include new and interesting problem formulations, new parametrizations of structural engineering optimization problems, and possibly new optimization methods for structural engineering. Presentations on bridging between academia and industrial applications on the subject are also welcome.

The symposium wishes to attract researchers active in this area of engineering and also to become a forum for information exchange and debate for both researchers and practitioners.

We are looking forward to meeting you in Chania.

To meet the industrial objectives in terms of competitiveness and environmental impact, multi-disciplinary design have already become key drivers for aircraft and engine manufacturers. So, the proposed symposium is dedicated to the development, validation and applications of adjoint-based optimization methods and their application in aerospace. Contributions in the field of multi-disciplinary optimization (MDO) are mostly expected, without though excluding original contributions in single discipline adjoint methods.

This MS is also open for papers in the field of unsteady adjoint, uncertainty quantification (UQ) and robust design optimization (RDO) as well as on methods for Pareto front tracing using adjoint.

Due to the multi-disciplinary nature of the subject, contributions from sectors other than aeronautics (such as papers in the field of car, and energy industries, etc) are also welcome.

This Mini-symposium (MS) on advanced design methods and optimisation tools for a climate neutral aviation is motivated by the need of a rapid transition of this sector towards addressing climate change in its future design. 

The integration and application of advanced multidisciplinary simulation, optimization, big data analysis, and artificial intelligence (AI) tools will have a significant impact on systemic procedures in industrial processes and the different aspects of aviation.

Multidisciplinary computing is one of the powerful design instruments used in academic research and in industrial areas of aviation and turbomachinery. The handling and analysis of multi-disciplinary big data is also gaining importance in the industrial production process, in operation and process control as well as in intelligent systems for surveillance and safety procedures.

Contributors to this MS will address innovative deterministic and stochastic methods combining experimental validation with numerical simulation, optimization, and control. 

Innovant computational methods will be addressed in the fields of simulation, optimization and control contributing to applications in aviation.   

Keywords: Aerodynamics, Flight Physics, Aero-Acoustics, Aero-elastics, Propulsion Systems, Structural Analysis, Multi-disciplinary Optimization and Control , Coupled Problems, Disruptive Concepts, Digital Twins.