The Epistemic Importance of Technology in Computer Simulation and Machine Learning

2019 ◽  
Vol 29 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Michael Resch ◽  
Andreas Kaminski
Keyword(s):  
Machine Learning ◽  
Computer Simulation

Evolutionary Computation and Genetic Algorithms

2011 ◽  
pp. 817-822
Author(s):  
William H. Hsu
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Computer Simulation ◽  
Genetic Algorithms ◽  
Natural Selection ◽  
Evolutionary Biology ◽  
Approximate Solutions ◽  
Learning Problems ◽  
Abstract Representations ◽  
Search Optimization

A genetic algorithm (GA) is a method used to find approximate solutions to difficult search, optimization, and machine learning problems (Goldberg, 1989) by applying principles of evolutionary biology to computer science. Genetic algorithms use biologically-derived techniques such as inheritance, mutation, natural selection, and recombination. They are a particular class of evolutionary algorithms. Genetic algorithms are typically implemented as a computer simulation in which a population of abstract representations (called chromosomes) of candidate solutions (called individuals) to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but different encodings are also possible. The evolution starts from a population of completely random individuals and happens in generations. In each generation, multiple individuals are stochastically selected from the current population, modified (mutated or recombined) to form a new population, which becomes current in the next iteration of the algorithm.

A Short Note on Optimizing Cost-Generalizability via a Machine-Learning Approach

2021 ◽  
pp. 001316442199211
Author(s):  
Zhehan Jiang ◽  
Dexin Shi ◽  
Christine Distefano
Keyword(s):  
Machine Learning ◽  
Computer Simulation ◽  
Simulated Annealing Algorithm ◽  
Generalizability Theory ◽  
Short Note ◽  
Optimal Solution ◽  
Generalizability Coefficient ◽  
Assessment Tasks ◽  
Machine Learning Approach ◽  
The Cost

The costs of an objective structured clinical examination (OSCE) are of concern to health profession educators globally. As OSCEs are usually designed under generalizability theory (G-theory) framework, this article proposes a machine-learning-based approach to optimize the costs, while maintaining the minimum required generalizability coefficient, a reliability-like index in G-theory. The authors adopted G-theory parameters yielded from an OSCE hosted by a medical school, reproduced the generalizability coefficients to prepare for optimizing manipulations, applied simulated annealing algorithm to calculate the number of facet levels minimizing the associated costs, and conducted the analysis in various conditions via computer simulation. With a given generalizability coefficient, the proposed approach, virtually an instrument of decision-making supports, found the optimal solution for the OSCE such that the associated costs were minimized. The computer simulation results showed how the cost reductions varied with different levels of required generalizability coefficients. Machine learning–based approaches can be used in conjunction with psychometric modeling to help planning assessment tasks more scientifically. The proposed approach is easy to adopt into practice and customize in alignment with specific testing designs. While these results are encouraging, the possible pitfalls such as algorithmic convergences’ failure and inadequate cost assumptions should also be avoided.

Computer simulation as a method for obtaining initial data for machine learning on the example of the conjugate problem of heat transfer of a finned tube

2020 ◽  
Author(s):  
A. F. Gizzatullina ◽  
M. R. Koroleva ◽  
O. V. Mishchenkova ◽  
E. A. Saburova
Keyword(s):  
Heat Transfer ◽  
Machine Learning ◽  
Computer Simulation ◽  
Initial Data ◽  
Conjugate Problem ◽  
Finned Tube

В работе проводится анализ современного состояния проблемы оценки тепло/энергоэффективности теплообменных аппаратов конвективного типа. Представлен подход к формированию базы данных параметров теплообмена оребренной трубки на основе результатов компьютерных исследований для обучения нейронной сети с целью построения функции отклика.

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.

Characterization of Modulated Structure Through Structure Images and their Computer Simulation

1990 ◽  
Vol 48 (1) ◽  
pp. 70-71
Author(s):  
Kiyomichi Nakai ◽  
Yusuke Isobe ◽  
Chiken Kinoshita ◽  
Kazutoshi Shinohara
Keyword(s):  
Computer Simulation ◽  
High Resolution ◽  
Spherical Aberration ◽  
High Resolution Electron Microscopy ◽  
Basic Mechanism ◽  
Objective Lens ◽  
Specimen Thickness ◽  
Transmitted Beam ◽  
Modulated Structure ◽  
Resolution Electron

Induced spinodal decomposition under electron irradiation in a Ni-Au alloy has been investigated with respect to its basic mechanism and confirmed to be caused by the relaxation of coherent strain associated with modulated structure. Modulation of white-dots on structure images of modulated structure due to high-resolution electron microscopy is reduced with irradiation. In this paper the atom arrangement of the modulated structure is confirmed with computer simulation on the structure images, and the relaxation of the coherent strain is concluded to be due to the reduction of phase-modulation.Structure images of three-dimensional modulated structure along <100> were taken with the JEM-4000EX high-resolution electron microscope at the HVEM Laboratory, Kyushu University. The transmitted beam and four 200 reflections with their satellites from the modulated structure in an fee Ni-30.0at%Au alloy under illumination of 400keV electrons were used for the structure images under a condition of the spherical aberration constant of the objective lens, Cs = 1mm, the divergence of the beam, α = 3 × 10-4 rad, underfocus, Δf ≃ -50nm and specimen thickness, t ≃ 15nm. The CIHRTEM code was used for the simulation of the structure image.

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