Illuminating the complex behaviour of particles in optical traps with machine learning

2020 ◽  
Author(s):  
Isaac C. Lenton ◽  
Giovanni Volpe ◽  
Alexander B. Stilgoe ◽  
Timo A. Nieminen ◽  
Halina Rubinsztein-Dunlop
Keyword(s):  
Machine Learning ◽  
Optical Traps ◽  
Complex Behaviour

Predicting particle properties in optical traps with machine learning

2020 ◽  
Author(s):  
Lachlan Hamilton ◽  
Lauren McQueen ◽  
Oscar Smee ◽  
Timo A. Nieminen ◽  
Halina Rubinsztein-Dunlop ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Traps ◽  
Particle Properties

scholarly journals Machine Learning Approach to Analyze the Different Optical Properties of FBGs

2021 ◽  
Author(s):  
Koustav Dey ◽  
V. Nikhil ◽  
Sourabh Roy
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Optical Properties ◽  
Fiber Bragg Gratings ◽  
Learning Approach ◽  
Complex Behaviour ◽  
Fast Training ◽  
Proposed Model ◽  
Machine Learning Approach

Abstract A generalized machine learning (ML) approach is proposed and demonstrated to analyse the various optical properties such as effective refractive index, bandwidth, reflectivity and wavelength of the Fiber Bragg gratings (FBGs). For this purpose, three commonly used FBG variants namely conventional, π phase-shifted and chirped FBG have been taken into consideration. Furthermore, the reflected spectra of those types of FBGs were predicted using a common tool. An exact spectrum was able to reproduce using this proposed model. This simple and fast-training feed-forward artificial neural network can predict the output for unknown device parameters along with the non-linear and complex behaviour of the spectrum minutely.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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