This brief note corrects the statements of Theorem 5.1 and Corollary 5.2 in (Tabuada and Gharesifard, 2023). The main consequence of these corrections is that the width of residual neural networks ...that suffices for universal approximation changes from Formula Omitted to Formula Omitted. This is consistent with recent observations made in (Hwang, 2023) regarding the use of neural networks to approximate functions by diffeomorphisms.
•Provides a review of machine learning methods used to perform risk assessments.•Automotive industry is leading the adoption of machine learning for risk assessments.•Risk assessments are commonly ...aided by artificial neural networks in the literature.•Machine learning methods often aid the risk identification phase during risk assessments.
The purpose of this article is to present a structured review of publications utilizing machine learning methods to aid in engineering risk assessment. A keyword search is performed to retrieve relevant articles from the databases of Scopus and Engineering Village. The search results are filtered according to seven selection criteria. The filtering process resulted in the retrieval of one hundred and twenty-four relevant research articles. Statistics based on different categories from the citation database is presented. By reviewing the articles, additional categories, such as the type of machine learning algorithm used, the type of input source used, the type of industry targeted, the type of implementation, and the intended risk assessment phase are also determined. The findings show that the automotive industry is leading the adoption of machine learning algorithms for risk assessment. Artificial neural networks are the most applied machine learning method to aid in engineering risk assessment. Additional findings from the review process are also presented in this article.
•Surveys on recent applications of artificial intelligence techniques to rotating machinery fault diagnosis.•Provides a guidance of how to choose and use artificial intelligence techniques in ...engineering.•Describes the artificial intelligence techniques applications and rotating machinery fault diagnosis trends.
Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k-nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. Finally, the advantages, limitations, practical implications of different AI algorithms, as well as some new research trends, are discussed.
We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how to incorporate the ...momentum balance and constitutive relations into PINN, and explore in detail the application to linear elasticity, and illustrate its extension to nonlinear problems through an example that showcases von Mises elastoplasticity. While common PINN algorithms are based on training one deep neural network (DNN), we propose a multi-network model that results in more accurate representation of the field variables. To validate the model, we test the framework on synthetic data generated from analytical and numerical reference solutions. We study convergence of the PINN model, and show that Isogeometric Analysis (IGA) results in superior accuracy and convergence characteristics compared with classic low-order Finite Element Method (FEM). We also show the applicability of the framework for transfer learning, and find vastly accelerated convergence during network re-training. Finally, we find that honoring the physics leads to improved robustness: when trained only on a few parameters, we find that the PINN model can accurately predict the solution for a wide range of parameters new to the network—thus pointing to an important application of this framework to sensitivity analysis and surrogate modeling.
•Application of Physics-Informed Neural Networks (PINNs) to solid mechanics.•Novel application to inversion, transfer learning, and surrogate modeling.•Formulation of PINNs for linear elasticity and von-Mises elastoplasticity.
Machine Learning methods and, in particular, Artificial Neural Networks (ANNs) have demonstrated promising capabilities in material constitutive modeling. One of the main drawbacks of such approaches ...is the lack of a rigorous frame based on the laws of physics. This may render physically inconsistent the predictions of a trained network, which can be even dangerous for real applications.
Here we propose a new class of data-driven, physics-based, neural networks for constitutive modeling of strain rate independent processes at the material point level, which we define as Thermodynamics-based Artificial Neural Networks (TANNs). The two basic principles of thermodynamics are encoded in the network’s architecture by taking advantage of automatic differentiation to compute the numerical derivatives of a network with respect to its inputs. In this way, derivatives of the free-energy, the dissipation rate and their relation with the stress and internal state variables are hardwired in the architecture of TANNs. Consequently, our approach does not have to identify the underlying pattern of thermodynamic laws during training, reducing the need of large data-sets. Moreover the training is more efficient and robust, and the predictions more accurate. Finally and more important, the predictions remain thermodynamically consistent, even for unseen data. Based on these features, TANNs are a starting point for data-driven, physics-based constitutive modeling with neural networks.
We demonstrate the wide applicability of TANNs for modeling elasto-plastic materials, using both hyper- and hypo-plasticity models. Strain hardening and softening are also considered for the hyper-plastic scenario. Detailed comparisons show that the predictions of TANNs outperform those of standard ANNs. Finally, we demonstrate that the implementation of the laws of thermodynamics confers to TANNs high robustness in the presence of noise in the training data, compared to standard approaches.
TANNs’ architecture is general, enabling applications to materials with different or more complex behavior, without any modification.
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•A new class of data-driven, physics-based, neural networks is proposed for constitutive modeling of strain rate independent processes at the material point level.•The two basic principles of thermodynamics are hardwired in the architecture of Thermodynamics-based Artificial Neural Networks.•The proposed approach is applied for modeling elasto-plastic materials, using both hyper- and hypo-plasticity models, with strain softening and hardening.•Detailed comparisons show that the predictions of the proposed class outperform those of standard neural network approaches.•The implementation of the laws of thermodynamics is found to confer to TANNs high robustness to noise and improved accuracy.
The performance and practicality of predictive energy management in hybrid electric vehicles (HEVs) are highly dependent on the forecast of future vehicular velocities, both in terms of accuracy and ...computational efficiency. In this brief, we provide a comprehensive comparative analysis of three velocity prediction strategies, applied within a model predictive control framework. The prediction process is performed over each receding horizon, and the predicted velocities are utilized for fuel economy optimization of a power-split HEV. We assume that no telemetry or on-board sensor information is available for the controller, and the actual future driving profile is completely unknown. Basic principles of exponentially varying, stochastic Markov chain, and neural network-based velocity prediction approaches are described. Their sensitivity to tuning parameters is analyzed, and the prediction precision, computational cost, and resultant vehicular fuel economy are compared.
In this paper, we developed dissimilar artificial neural networks (ANNs) by suitable architectures and training algorithms via sensitivity analysis to predict the thermal conductivity MWCNT -TiO2/ ...Water-Ethylene glycol nanofluid. Forecasting of thermal conductivity of MWCNT –TiO2/ Water-Ethylene glycol nanofluid based on changes in temperature and concentration using ANN and stability analysis is done. MWCNTs-TiO2 hybrid nanoparticles were also used at a 50:50 volume ratio. The dataset of ANN was divided into three main parts including 70% for the train, 15% for test and 15% for validation and the results of the optimum ANN are in a better agreement to the empirical dataset, and it can predict the thermal conductivity of MWCNT-TiO2-Wa-EG(50–50) better than the correlation. The empirical dataset, ANN outputs, and correlation results were presented. There is a small difference between correlation results and ANN outputs, and it can be concluded that ANN outputs are can predict the empirical results better than the correlation formula.
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•Forecasting of thermal conductivity of nanofluid based on changes in temperature and concentration.•Using artificial neural network and stability analysis.•Investigation of stability of MWCNT -TiO2/ Ethylene glycol nanofluid.
•Propose a deep classification algorithm for mammogram images.•The deep classification performance is improved by the feature wise pre-processing.•Application of proposed technique to detect and ...classify breast cancer.•An intricate designed classification trained using extracted features.•Achieved classification accuracy of 90.50% and specificity of 90.71%.
Traditionally, physicians need to manually delineate the suspected breast cancer area. Numerous studies have mentioned that manual segmentation takes time, and depends on the machine and the operator. The algorithm called Convolutional Neural Network Improvement for Breast Cancer Classification (CNNI-BCC) is presented to assist medical experts in breast cancer diagnosis in timely manner. The CNNI-BCC uses a convolutional neural network that improves the breast cancer lesion classification in order to help experts for breast cancer diagnosis. CNNI-BCC can classify incoming breast cancer medical images into malignant, benign, and healthy patients. The application of present algorithm can assist in classification of mammographic medical images into benign patient, malignant patient and healthy patient without prior information of the presence of a cancerous lesion. The presented method aims to help medical experts for the classification of breast cancer lesion through the implementation of convolutional neural network for the classification of breast cancer. CNNI-BCC can categorize incoming medical images as malignant, benign or normal patient with sensitivity, accuracy, area under the receiver operating characteristic curve (AUC) and specificity of 89.47%, 90.50%, 0.901 ± 0.0314 and 90.71% respectively.
Recent technological advances have enabled DNA methylation to be assayed at single-cell resolution. However, current protocols are limited by incomplete CpG coverage and hence methods to predict ...missing methylation states are critical to enable genome-wide analyses. We report DeepCpG, a computational approach based on deep neural networks to predict methylation states in single cells. We evaluate DeepCpG on single-cell methylation data from five cell types generated using alternative sequencing protocols. DeepCpG yields substantially more accurate predictions than previous methods. Additionally, we show that the model parameters can be interpreted, thereby providing insights into how sequence composition affects methylation variability.
Multiple linear regressions are an important tool used to find the relationship between a set of variables used in various scientific experiments. In this article we are going to introduce a simple ...method of solving a multiple rectilinear regressions (MLR) problem that uses an artificial neural network to find the accurate and expected output from MLR problem. Different artificial neural network (ANN) types with different architecture will be tested, the error between the target outputs and the calculated ANN outputs will be investigated. A recommendation of using a certain type of ANN based on the experimental results will be raised.
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