Inductive Discovery by Machine Learning for Identification of Structural Models

2018 ◽  
pp. 545-552 ◽  
Author(s):  
Wolfgang Maass ◽  
Iaroslav Shcherbatyi
Keyword(s):  
Machine Learning ◽  
Structural Models

scholarly journals Short range order and network connectivity in amorphous AsTe3: a first principles, machine learning, and XRD study

2020 ◽  
Vol 22 (43) ◽  
pp. 24895-24906
Author(s):  
Gaëlle Delaizir ◽  
Andrea Piarristeguy ◽  
Annie Pradel ◽  
Olivier Masson ◽  
Assil Bouzid
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
Short Range ◽  
Structural Models ◽  
Network Connectivity ◽  
Atomic Scale ◽  
Interatomic Potentials ◽  
X Ray Diffraction ◽  
X Ray ◽  
Xrd Study

The atomic scale structure of amorphous AsTe3 is investigated through coupling X-ray diffraction, and realistic structural models issued from ab initio molecular dynamics and machine learning based interatomic potentials.

scholarly journals Artificial Intelligence Based Space Reduction of Structural Models

2021 ◽  
Author(s):  
Chady Ghnatios ◽  
George El Haber ◽  
Jean-Louis Duval ◽  
Mustapha Ziane ◽  
Francisco Chinesta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Structural Models ◽  
Machine Learning Techniques ◽  
Coarse Grained ◽  
Fine Scale ◽  
Space Reduction ◽  
Learning Techniques ◽  
Out Of Plane ◽  
Computational Resources

The need of solving industrial problems using faster and less computationally expensive techniques is becoming a requirement to cope with the present digital transformation of most industries. Recently, data is conquering the domain of engineering with different purposes: (i) defining data-driven models of materials, processes, structures and systems, whose physics-based models, when they exists, remain too inaccurate; (ii) enriching the existing physics-based models within the so-called hybrid paradigm; and (iii) using advanced machine learning and artificial intelligence techniques for scales bridging (upscaling), that is, for creating models that operating at the coarse-grained scale (cheaper in what respect the computational resources) enables integrating the fine-scale richness. The present work addresses the last item, aiming at enhancing standard structural models (defined in 2D shell geometries) for accounting all the fine-scale details (3D with rich through-the-thickness behaviors). For this purpose, two main strategies will be combined: (i) the in-plane-out-of-plane proper generalized decomposition -PGD- serving to provide the fine-scale richness; and (ii) advance machine learning techniques able to learn and extract the regression relating the input parameters with those high-resolution detailed descriptions.

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

Validation of the Short Dark Triad in a German Sample

2020 ◽  
pp. 1-12
Author(s):  
Caroline Wehner ◽  
Ulrike Maaß ◽  
Marius Leckelt ◽  
Mitja D. Back ◽  
Matthias Ziegler
Keyword(s):  
Sensation Seeking ◽  
Factor Model ◽  
Structural Models ◽  
Self Esteem ◽  
Dark Triad ◽  
Fit Indices ◽  
Nomological Network ◽  
Competitive Test ◽  
Three Factor ◽  
Behavioral Criteria

Abstract. The structure, correlates, and assessment of the Dark Triad are widely discussed in several fields of psychology. Based on the German version of the Short Dark Triad (SDT), we add to this by (a) providing a competitive test of existing structural models, (b) testing the nomological network, and (c) proposing an ultrashort 9-item version of the SDT (uSDT). A sample of N = 969 participants provided data on the SDT and a range of further measures. Our competitive test of five structural models revealed that fit indices and nomological network assumptions were best met in a three-factor model, with separate factors for psychopathy, Machiavellianism, and narcissism. The results provided an extensive overview of the raw, unique, and shared associations of Dark Triad dimensions with narcissism facets, sadism, impulsivity, self-esteem, sensation seeking, the Big Five, maladaptive personality traits, sociosexual orientation, and behavioral criteria. Finally, the uSDT exhibited satisfactory psychometric properties. The highest overlap in expected relations between SDT and uSDT, and convergent and discriminant measures was also found for the three-factor model. Our study underlines the utility of a three-factor model of the Dark Triad, extends findings on its nomological network, and provides an ultrashort instrument.

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