A state-of-the-art-review on phase change materials integrated cooling systems for deterministic parametrical analysis, stochastic uncertainty-based design, single and multi-objective optimisations with machine learning applications

Metasurfaces offer the potential to realize large SWaP (size, weight, and power) reduction over conventional optical elements for their ability to achieve comparable functionalities in ultrathin geometries. Moreover, metasurfaces designed with phase change materials offer the potential to go beyond what is achievable by conventional optics by enabling multiple functionalities in a single reconfigurable meta-device. However, designing a single metasurface geometry that simultaneously achieves multiple desired functionalities while meeting all bandwidth requirements and fabrication constraints is a very challenging problem. Fortunately, this challenge can be overcome by the use of state-of-the-art multi-objective optimization algorithms which are well-suited for the inverse-design of multifunctional meta-devices.

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Passive and active phase change materials integrated building energy systems with advanced machine-learning based climate-adaptive designs, intelligent operations, uncertainty-based analysis and optimisations: A state-of-the-art review

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2020.109889 ◽

2020 ◽

Vol 130 ◽

pp. 109889 ◽

Cited By ~ 10

Author(s):

Yuekuan Zhou ◽

Siqian Zheng ◽

Zhengxuan Liu ◽

Tao Wen ◽

Zhixiong Ding ◽

...

Keyword(s):

Machine Learning ◽

Phase Change ◽

Phase Change Materials ◽

State Of The Art ◽

Energy Systems ◽

Active Phase ◽

Building Energy ◽

Adaptive Designs ◽

Building Energy Systems

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Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽

10.3390/a12050099 ◽

2019 ◽

Vol 12 (5) ◽

pp. 99 ◽

Cited By ~ 2

Author(s):

Kleopatra Pirpinia ◽

Peter A. N. Bosman ◽

Jan-Jakob Sonke ◽

Marcel van Herk ◽

Tanja Alderliesten

Keyword(s):

Machine Learning ◽

Image Registration ◽

State Of The Art ◽

Deformable Image Registration ◽

Optimization Approach ◽

High Quality ◽

Trade Off ◽

Multi Objective ◽

Current State ◽

Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

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