An efficient optimization approach for designing machine learning models based on genetic algorithm

Hamdia, Khader M.; Zhuang, Xiaoying; Rabczuk, Timon

doi:10.1007/s00521-020-05035-x

Cited by 198 publications

(85 citation statements)

References 46 publications

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“…DL has many architectures, especially in mechanics, and research is focused mainly on using the feed-forward neural network (FFNN), recurrent neural network (RNN), and convolutional neural network (CNN). So far, applications of these types of neural networks devoted to the field of mechanics can be seen in the recent works of [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43]. A detailed description of those approaches is summarized as follows:…”

Section: Deep Learning (Dl) Architecturesmentioning

confidence: 99%

Feed-Forward Neural Networks for Failure Mechanics Problems

2021

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This work addresses an efficient neural network (NN) representation for the phase-field modeling of isotropic brittle fracture. In recent years, data-driven approaches, such as neural networks, have become an active research field in mechanics. In this contribution, deep neural networks—in particular, the feed-forward neural network (FFNN)—are utilized directly for the development of the failure model. The verification and generalization of the trained models for elasticity as well as fracture behavior are investigated by several representative numerical examples under different loading conditions. As an outcome, promising results close to the exact solutions are produced.

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Section: Deep Learning (Dl) Architecturesmentioning

confidence: 99%

Feed-Forward Neural Networks for Failure Mechanics Problems

2021

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“…It does not need to specify basic functions in advance, but based on experimental data, after a limited number of iterative calculations, a mathematical model that reflects the internal connection of experimental data is obtained. In theory, artificial neural network technology can handle arbitrarily complex multivariate nonlinear relationships [8].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

Liu

Guang-ping

2021

Computational Intelligence and Neuroscience

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In the 21st century, with the increasingly urgent requirements for lightweight in the fields of aviation, aerospace, and electronics, especially automobiles, many properties of magnesium alloy materials, especially the low-density performance characteristics, have been widely valued. In order to better use magnesium metal materials, it is very important to evaluate its mechanical properties. This article is based on 196 sets of mechanical performance experimental results and related data of AZ31 and AZ91 2 magnesium alloys. Based on data analysis and sorting, take deformation temperature, deformation rate, deformation coefficient, solid solution temperature, and solid solution time as input and take ultimate tensile strength (UTS), yield strength (YS), and elongation (ELO) as output. The 5-8-1 three-layer BP neural network forecast model optimized by the genetic algorithm is used for data training. The training results show that the prediction model can accurately predict the tensile strength, yield strength, and elongation. Compared with the general BP neural network prediction model, the BP neural network based on the genetic algorithm has small discreteness and high fitness: the average error of UTS and YS of AZ31 magnesium alloy is reduced to 0.88% and 3.3%, respectively. The most obvious is that the elongation of AZ31 ELO is reduced, and the error is reduced to 8.1%.

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“…These weighted connections represent the connecting interactions. The optimal weights of each connection between a set of layers are calculated during each backward pass of a training dataset, which is also used for weight optimisation using the derivatives obtained from the input and predicted values of the training data [24]. The layers represent the network topology, representing neuron interconnections.…”

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

The stochastic aeroelastic response analysis of helicopter rotors using deep and shallow machine learning

Chatterjee

Essien

Ganguli

et al. 2021

Neural Comput & Applic

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This paper addresses the influence of manufacturing variability of a helicopter rotor blade on its aeroelastic responses. An aeroelastic analysis using finite elements in spatial and temporal domains is used to compute the helicopter rotor frequencies, vibratory hub loads, power required and stability in forward flight. The novelty of the work lies in the application of advanced data-driven machine learning (ML) techniques, such as convolution neural networks (CNN), multi-layer perceptron (MLP), random forests, support vector machines and adaptive Gaussian process (GP) for capturing the nonlinear responses of these complex spatio-temporal models to develop an efficient physics-informed ML framework for stochastic rotor analysis. Thus, the work is of practical significance as (i) it accounts for manufacturing uncertainties, (ii) accurately quantifies their effects on nonlinear response of rotor blade and (iii) makes the computationally expensive simulations viable by the use of ML. A rigorous performance assessment of the aforementioned approaches is presented by demonstrating validation on the training dataset and prediction on the test dataset. The contribution of the study lies in the following findings: (i) The uncertainty in composite material and geometric properties can lead to significant variations in the rotor aeroelastic responses and thereby highlighting that the consideration of manufacturing variability in analyzing helicopter rotors is crucial for assessing their behaviour in real-life scenarios. (ii) Precisely, the substantial effect of uncertainty has been observed on the six vibratory hub loads and the damping with the highest impact on the yawing hub moment. Therefore, sufficient factor of safety should be considered in the design to alleviate the effects of perturbation in the simulation results. (iii) Although advanced ML techniques are harder to train, the optimal model configuration is capable of approximating the nonlinear response trends accurately. GP and CNN followed by MLP achieved satisfactory performance. Excellent accuracy achieved by the above ML techniques demonstrates their potential for application in the optimization of rotors under uncertainty.

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An efficient optimization approach for designing machine learning models based on genetic algorithm

Cited by 198 publications

References 46 publications

Feed-Forward Neural Networks for Failure Mechanics Problems

Feed-Forward Neural Networks for Failure Mechanics Problems

[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

The stochastic aeroelastic response analysis of helicopter rotors using deep and shallow machine learning

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