Figure from paper presented at ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)SummaryThe figure illustrates a conceptual framework to guide the integration of simulation models with ML. At its core, our approach is based on the premise that system knowledge can be (partially) captured and learned from data in an automated manner aided by ML. We conceive that the approach can help realise adaptive simulation models that learn to change their behaviour in response to behavioural changes in the actual system of interest.</div
International audienceNumerical simulations play more and more important role in product development...
Machine Learning (ML) is about computational methods that enable machines to learn concepts from exp...
The paper uses ideas from Machine Learning, Artificial Intelligence and Genetic Algorithms to provid...
<div>Presented at ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)<br></div>...
<div>Learning about Systems Using Machine Learning:Towards More Data-Driven Feedback Loops<br></div>...
Knowledge acquisition for an understanding of discrete event simulation systems is a difficult task....
Source:Learning about Systems Using Machine Learning:Towards More Data-Driven Feedback LoopsIn Proce...
Recent trends towards data-driven methods may require a substantial rethinking of the process of dev...
In this paper, we describe the combination of machine learning and simulation towards a hybrid model...
The current thesis investigates data-driven simulation decision-making with field-quality consumer d...
Computer simulations are an often applied and promising form of CAL. A main characteristic of comput...
Poster presented at Sixth European Business Intelligence & Big Data Summer School (eBISS 2016)Unive...
From simple clustering techniques to more sophisticated neural networks, the use of machine learning...
Presented at Winter Simulation Conference 2018, Gothenburg, SwedenAuthors:Mahmoud Elbattah, Owen Mol...
This master’s thesis describes the process of integrating data generated by machine learning models ...
International audienceNumerical simulations play more and more important role in product development...
Machine Learning (ML) is about computational methods that enable machines to learn concepts from exp...
The paper uses ideas from Machine Learning, Artificial Intelligence and Genetic Algorithms to provid...
<div>Presented at ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)<br></div>...
<div>Learning about Systems Using Machine Learning:Towards More Data-Driven Feedback Loops<br></div>...
Knowledge acquisition for an understanding of discrete event simulation systems is a difficult task....
Source:Learning about Systems Using Machine Learning:Towards More Data-Driven Feedback LoopsIn Proce...
Recent trends towards data-driven methods may require a substantial rethinking of the process of dev...
In this paper, we describe the combination of machine learning and simulation towards a hybrid model...
The current thesis investigates data-driven simulation decision-making with field-quality consumer d...
Computer simulations are an often applied and promising form of CAL. A main characteristic of comput...
Poster presented at Sixth European Business Intelligence & Big Data Summer School (eBISS 2016)Unive...
From simple clustering techniques to more sophisticated neural networks, the use of machine learning...
Presented at Winter Simulation Conference 2018, Gothenburg, SwedenAuthors:Mahmoud Elbattah, Owen Mol...
This master’s thesis describes the process of integrating data generated by machine learning models ...
International audienceNumerical simulations play more and more important role in product development...
Machine Learning (ML) is about computational methods that enable machines to learn concepts from exp...
The paper uses ideas from Machine Learning, Artificial Intelligence and Genetic Algorithms to provid...