Computational Optimization, Modelling and Simulation: Recent Trends and Challenges
Modelling, simulation and optimization form an integrated part of modern design practice in engineering and industry. Tremendous progress has been observed for all three components over the last few decades. However, many challenging issues remain unresolved, and the current trends tend to use nature-inspired algorithms and surrogate-based techniques for modelling and optimization. This 4th workshop on Computational Optimization, Modelling and Simulation (COMS 2013) at ICCS 2013 will further summarize the latest developments of optimization and modelling and their applications in science, engineering and industry. In this review paper, we will analyse the recent trends in modelling and optimization, and their associated challenges. We will discuss important topics for further research, including parameter-tuning, large-scale problems, and the gaps between theory and applications.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A review on modelling, evaluation, and optimization of cyber-physical system reliability
The aim of this study is to present an overview of current research on modelling, evaluation, and optimization methods for improving the reliability of Cyber-Physical System (CPS). Three major modelling approaches, namel…
Trends, Applications, and Challenges in Human Attention Modelling
Human attention modelling has proven, in recent years, to be particularly useful not only for understanding the cognitive processes underlying visual exploration, but also for providing support to artificial intelligence…
Language ModellingMachine Learning Applications to Computational Plasma Physics and Reduced-Order Plasma Modeling: A Perspective
Machine learning (ML) provides a broad spectrum of tools and architectures that enable the transformation of data from simulations and experiments into useful and explainable science, thereby augmenting domain knowledge.…
Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities
Recent advances in machine learning, coupled with low-cost computation, availability of cheap streaming sensors, data storage and cloud technologies, has led to widespread multi-disciplinary research activity with signif…
BIG-bench Machine LearningAn Embedded Model Estimator for Non-Stationary Random Functions using Multiple Secondary Variables
An algorithm for non-stationary spatial modelling using multiple secondary variables is developed. It combines Geostatistics with Quantile Random Forests to give a new interpolation and stochastic simulation algorithm. T…