Training Data Selection for Machine Learning-Enhanced Monte Carlo Simulations in Structural Dynamics

The evaluation of structural response constitutes a fundamental task in the design of ground-excited structures. In this context, the Monte Carlo simulation is a powerful tool to estimate the response statistics of nonlinear systems, which cannot be represented analytically. Unfortunately, the number of samples which is required for estimations with high confidence increases disproportionally to obtain a reliable estimation of low-probability events. As a consequence, the Monte Carlo simulation becomes a non-realizable task from a computational perspective. We show that the application of machine learning algorithms significantly lowers the computational burden of the Monte Carlo method. We use artificial neural networks to predict structural response behavior using supervised learning. However, one shortcoming of supervised learning is the inability of a sufficiently accurate prediction when extrapolating to data the neural network has not seen yet. In this paper, neural networks predict the response of structures subjected to non-stationary ground excitations. In doing so, we propose a novel selection process for the training data to provide the required samples to reliably predict rare events. We, finally, prove that the new strategy results in a significant improvement of the prediction of the response statistics in the tail end of the distribution.

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Convolutional Neural Network

10.4018/978-1-6684-2408-7.ch077 ◽

2022 ◽

pp. 1559-1575

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

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Machine Learning in Gifted Education: A Demonstration Using Neural Networks

Gifted Child Quarterly ◽

10.1177/0016986219867483 ◽

2019 ◽

Vol 63 (4) ◽

pp. 243-252 ◽

Cited By ~ 1

Author(s):

Jaret Hodges ◽

Soumya Mohan

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Supervised Learning ◽

Language Processing ◽

Gifted Education ◽

Learning Algorithms ◽

Simulated Data ◽

Machine Learning Algorithms ◽

Automated Driving ◽

The Neural Network

Machine learning algorithms are used in language processing, automated driving, and for prediction. Though the theory of machine learning has existed since the 1950s, it was not until the advent of advanced computing that their potential has begun to be realized. Gifted education is a field where machine learning has yet to be utilized, even though one of the underlying problems of gifted education is classification, which is an area where learning algorithms have become exceptionally accurate. We provide a brief overview of machine learning with a focus on neural networks and supervised learning, followed by a demonstration using simulated data and neural networks for classification issues with a practical explanation of the mechanics of the neural network and associated R code. Implications for gifted education are then discussed. Finally, the limitations of supervised learning are discussed. Code used in this article can be found at https://osf.io/4pa3b/

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SimML Framework: Monte Carlo Simulation of Statistical Machine Learning Algorithms for IoT Prognostic Applications

2016 International Conference on Computational Science and Computational Intelligence (CSCI) ◽

10.1109/csci.2016.0040 ◽

2016 ◽

Cited By ~ 2

Author(s):

Ashwini R. More ◽

Kenny C. Gross

Keyword(s):

Machine Learning ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Statistical Machine Learning

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Creep Rupture Forecasting

International Journal of Monitoring and Surveillance Technologies Research ◽

10.4018/ijmstr.2014040101 ◽

2014 ◽

Vol 2 (2) ◽

pp. 1-25 ◽

Cited By ~ 2

Author(s):

Stylianos Chatzidakis ◽

Miltiadis Alamaniotis ◽

Lefteri H. Tsoukalas

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Process Model ◽

Production Systems ◽

Learning Algorithms ◽

Difficult Problem ◽

Network Size ◽

Creep Rupture ◽

Machine Learning Algorithms ◽

Training Data

Creep rupture is becoming increasingly one of the most important problems affecting behavior and performance of power production systems operating in high temperature environments and potentially under irradiation as is the case of nuclear reactors. Creep rupture forecasting and estimation of the useful life is required to avoid unanticipated component failure and cost ineffective operation. Despite the rigorous investigations of creep mechanisms and their effect on component lifetime, experimental data are sparse rendering the time to rupture prediction a rather difficult problem. An approach for performing creep rupture forecasting that exploits the unique characteristics of machine learning algorithms is proposed herein. The approach seeks to introduce a mechanism that will synergistically exploit recent findings in creep rupture with the state-of-the-art computational paradigm of machine learning. In this study, three machine learning algorithms, namely General Regression Neural Networks, Artificial Neural Networks and Gaussian Processes, were employed to capture the underlying trends and provide creep rupture forecasting. The current implementation is demonstrated and evaluated on actual experimental creep rupture data. Results show that the Gaussian process model based on the Matérn kernel achieved the best overall prediction performance (56.38%). Significant dependencies exist on the number of training data, neural network size, kernel selection and whether interpolation or extrapolation is performed.

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Performance of Machine Learning Algorithms and Diversity in Data

MATEC Web of Conferences ◽

10.1051/matecconf/201821004019 ◽

2018 ◽

Vol 210 ◽

pp. 04019 ◽

Cited By ~ 1

Author(s):

Hyontai SUG

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Real World ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Data ◽

Real World Data ◽

Random Data ◽

Data Set ◽

World Data

Recent world events in go games between human and artificial intelligence called AlphaGo showed the big advancement in machine learning technologies. While AlphaGo was trained using real world data, AlphaGo Zero was trained using massive random data, and the fact that AlphaGo Zero won AlphaGo completely revealed that diversity and size in training data is important for better performance for the machine learning algorithms, especially in deep learning algorithms of neural networks. On the other hand, artificial neural networks and decision trees are widely accepted machine learning algorithms because of their robustness in errors and comprehensibility respectively. In this paper in order to prove that diversity and size in data are important factors for better performance of machine learning algorithms empirically, the two representative algorithms are used for experiment. A real world data set called breast tissue was chosen, because the data set consists of real numbers that is very good property for artificial random data generation. The result of the experiment proved the fact that the diversity and size of data are very important factors for better performance.

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Rapid evaluation method for anisotropic growth of WS2 monolayers by combining machine learning algorithms and kinetic Monte Carlo simulation data

Computational Materials Science ◽

10.1016/j.commatsci.2020.109922 ◽

2020 ◽

Vol 184 ◽

pp. 109922

Author(s):

Yuanfeng Xia ◽

Lixiang Wu ◽

Gaofeng Wang

Keyword(s):

Machine Learning ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Evaluation Method ◽

Kinetic Monte Carlo ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Rapid Evaluation ◽

Anisotropic Growth ◽

Simulation Data

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Neural Networks for parameter estimation in microstructural MRI: a study with a high-dimensional diffusion-relaxation model of white matter microstructure

10.1101/2021.03.12.435163 ◽

2021 ◽

Cited By ~ 1

Author(s):

João P. de Almeida Martins ◽

Markus Nilsson ◽

Björn Lampinen ◽

Marco Palombo ◽

Peter T. While ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Parameter Estimation ◽

White Matter ◽

Machine Learning Algorithms ◽

Training Data ◽

High Dimensional ◽

Acquisition Protocol ◽

White Matter Microstructure ◽

The Impact

ABSTRACTSpecific features of white-matter microstructure can be investigated by using biophysical models to interpret relaxation-diffusion MRI brain data. Although more intricate models have the potential to reveal more details of the tissue, they also incur time-consuming parameter estimation that may con-verge to inaccurate solutions due to a prevalence of local minima in a degenerate fitting landscape. Machine-learning fitting algorithms have been proposed to accelerate the parameter estimation and increase the robustness of the attained estimates. So far, learning-based fitting approaches have been restricted to lower-dimensional microstructural models where dense sets of training data are easy to generate. Moreover, the degree to which machine learning can alleviate the degeneracy problem is poorly understood. For conventional least-squares solvers, it has been shown that degeneracy can be avoided by acquisition with optimized relaxation-diffusion-correlation protocols that include tensor-valued diffusion encoding; whether machine-learning techniques can offset these acquisition require-ments remains to be tested. In this work, we employ deep neural networks to vastly accelerate the fitting of a recently introduced high-dimensional relaxation-diffusion model of tissue microstructure. We also develop strategies for assessing the accuracy and sensitivity of function fitting networks and use those strategies to explore the impact of acquisition protocol design on the performance of the network. The developed learning-based fitting pipelines were tested on relaxation-diffusion data acquired with optimized and sub-sampled acquisition protocols. We found no evidence that machine-learning algorithms can by themselves replace a careful design of the acquisition protocol or correct for a degenerate fitting landscape.

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