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A benchmark-based method for evaluating hyperparameter optimization techniques of neural networks for surface water quality prediction

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Frontiers of Environmental Science & Engineering 2024年第5期18卷 13-27页

作者： Xuan Wang Yan Dong Jing Yang Zhipeng Liu Jinsuo Lu School of Environmental and Municipal Engineering Xi’an University of Architecture and TechnologyXi’an710055China Shaanxi Key Laboratory of Environmental Engineering Xi’an University of Architecture and TechnologyXi’an710055China State Key Laboratory of Green Building in West China Xi’an University of Architecture and TechnologyXi’an710055China

neural networks(NNs)have been used extensively in surface water prediction tasks due to computing algorithm improvements and data accumulation.An essential step in developing an NN is the hyperparameter selection.In practice,it is common to manually determine hyperparameters in the studies of NNs in water resources tasks.This may result in considerable randomness and require significant computation time;therefore,hyperparameter optimization(HPO)is essential.This study adopted five representatives of the HPO techniques in the surface water quality prediction tasks,including the grid sampling(GS),random search(RS),genetic algorithm(GA),Bayesian optimization(BO)based on the Gaussian process(GP),and the tree Parzen estimator(TPE).For the evaluation of these techniques,this study proposed a method:first,the optimal hyperparameter value sets achieved by GS were regarded as the benchmark;then,the other HPO techniques were evaluated and compared with the benchmark in convergence,optimization orientation,and consistency of the optimized values.The results indicated that the TPE-based BO algorithm was recommended because it yielded stable convergence,reasonable optimization orientation,and the highest consistency rates with the benchmark values.The optimization consistency rates via TPE for the hyperparameters hidden layers,hidden dimension,learning rate,and batch size were 86.7%,73.3%,73.3%,and 80.0%,respectively.Unlike the evaluation of HPO techniques directly based on the prediction performance of the optimized NN in a single HPO test,the proposed benchmark-based HPO evaluation approach is feasible and robust.

关键词： neural networks Hyperparameter optimization Surface water quality prediction Bayes optimization Genetic algorithm

NeuroPNM:Model reduction of pore network models using neural networks

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Particuology 2024年第3期86卷 239-251页

作者： Robert Jendersie Ali Mjalled Xiang Lu Lucas Reineking Abdolreza Kharaghani Martin Monnigmann Christian Lessig Otto-von-Guericke-Universitat Magdeburg Universitatsplatz 2Magdeburg39106Germany Ruhr-Universitat-Bochum Universitatstraβe 150Bochum44801Germany

Reacting particle systems play an important role in many industrial applications,for example biomass drying or the manufacturing of pharmaceuticals.The numerical modeling and simulation of such systems is therefore of great importance for an efficient,reliable,and environmentally sustainable operation of the processes.The complex thermodynamical,chemical,and flow processes that take place in the particles are a particular challenge in a simulation.Furthermore,typically a large number of particles is involved,rendering an explicit treatment of individual ones impossible in a reactor-level simulation.One approach for overcoming this challenge is to compute effective,physical parameters from single-particle,high-resolution simulations.This can be combined with model reduction methods if the dynamical behaviour of particles must be captured.Pore network models with their unrivaled resolution have thereby been used successfully as high-resolution models,for instance to obtain the macroscopic diffusion coeffcient of drying.Both parameter identification and model reduction have recently gained new impetus by the dramatic progress made in machine learning in the last decade.We report results on the use of neural networks for parameter identification and model reduction based on three-dimensional pore network models(PNM).We believe that our results provide a powerful complement to existing methodologies for reactor-level simulations with many thermally-thick particles.

关键词： Porenetwork models neural networks Parameter estimation Reduced order model

A Review of Computing with Spiking neural networks

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Computers, Materials & Continua 2024年第3期78卷 2909-2939页

作者： Jiadong Wu Yinan Wang Zhiwei Li Lun Lu Qingjiang Li College of Electronic Science and Technology National University of Defense TechnologyChangsha410073China

Artificial neural networks(ANNs)have led to landmark changes in many fields,but they still differ significantly fromthemechanisms of real biological neural networks and face problems such as high computing costs,excessive computing power,and so on.Spiking neural networks(SNNs)provide a new approach combined with brain-like science to improve the computational energy efficiency,computational architecture,and biological credibility of current deep learning applications.In the early stage of development,its poor performance hindered the application of SNNs in real-world scenarios.In recent years,SNNs have made great progress in computational performance and practicability compared with the earlier research results,and are continuously producing significant results.Although there are already many pieces of literature on SNNs,there is still a lack of comprehensive review on SNNs from the perspective of improving performance and practicality as well as incorporating the latest research results.Starting from this issue,this paper elaborates on SNNs along the complete usage process of SNNs including network construction,data processing,model training,development,and deployment,aiming to provide more comprehensive and practical guidance to promote the development of SNNs.Therefore,the connotation and development status of SNNcomputing is reviewed systematically and comprehensively from four aspects:composition structure,data set,learning algorithm,software/hardware development platform.Then the development characteristics of SNNs in intelligent computing are summarized,the current challenges of SNNs are discussed and the future development directions are also prospected.Our research shows that in the fields of machine learning and intelligent computing,SNNs have comparable network scale and performance to ANNs and the ability to challenge large datasets and a variety of tasks.The advantages of SNNs over ANNs in terms of energy efficiency and spatial-temporal data processing have been more fully exploited.And the developme

关键词： Spiking neural networks neural networks brain-like computing artificial intelligence learning algorithm

A REDUCED ORDER SCHWARZ METHOD FOR NONLINEAR MULTISCALE ELLIPTIC EQUATIONS BASED ON TWO-LAYER neural networks

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Journal of Computational Mathematics 2024年第2期42卷 570-596页

作者： Shi Chen Zhiyan Ding Qin Li Stephen J.Wright Mathematics Department University of Wisconsin-MadisonMadisonWI 53706USA Department of Mathematics University of California-BerkeleyBerkeley CA 94720USA Mathematics Department and Discovery Institute University of Wisconsin-Madison MadisonWI 53706USA Computer Sciences Department University of WisconsinMadisonWI 53706USA

neural networks are powerful tools for approximating high dimensional data that have been used in many contexts,including solution of partial differential equations(PDEs).We describe a solver for multiscale fully nonlinear elliptic equations that makes use of domain decomposition,an accelerated Schwarz framework,and two-layer neural networks to approximate the boundary-to-boundarymap for the subdomains,which is the key step in the Schwarz procedure.Conventionally,the boundary-to-boundary map requires solution of boundary-value elliptic problems on each subdomain.By leveraging the compressibility of multiscale problems,our approach trains the neural network offline to serve as a surrogate for the usual implementation of the boundary-to-boundary map.Our method is applied to a multiscale semilinear elliptic equation and a multiscale p-Laplace equation.In both cases we demonstrate significant improvement in efficiency as well as good accuracy and generalization performance.

关键词： Nonlinear homogenization Multiscale elliptic problem neural networks Do-main decomposition

Predicting microseismic,acoustic emission and electromagnetic radiation data using neural networks

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第2期16卷 616-629页

作者： Yangyang Di Enyuan Wang Zhonghui Li Xiaofei Liu Tao Huang Jiajie Yao School of Materials Engineering Changshu Institute of TechnologySuzhou215506China Key Laboratory of Gas and Fire Control for Coal Mines(China University of Mining and Technology) Ministry of EducationXuzhou221116China School of Safety Engineering China University of Mining and TechnologyXuzhou221116China State Key Laboratory of Coal Resources and Safe Mining China University of Mining and TechnologyXuzhou221116China

Microseism,acoustic emission and electromagnetic radiation(M-A-E)data are usually used for predicting rockburst hazards.However,it is a great challenge to realize the prediction of M-A-E data.In this study,with the aid of a deep learning algorithm,a new method for the prediction of M-A-E data is proposed.In this method,an M-A-E data prediction model is built based on a variety of neural networks after analyzing numerous M-A-E data,and then the M-A-E data can be predicted.The predicted results are highly correlated with the real data collected in the field.Through field verification,the deep learning-based prediction method of M-A-E data provides quantitative prediction data for rockburst monitoring.

关键词： Microseism Acoustic emission Electromagnetic radiation neural networks Deep learning Rockburst

Learning topological defects formation with neural networks in a quantum phase transition

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Communications in Theoretical Physics 2024年第5期76卷 68-76页

作者： Han-Qing Shi Hai-Qing Zhang Center for Gravitational Physics Department of Space ScienceBeihang UniversityBeijing 100191China Peng Huanwu Collaborative Center for Research and Education Beihang UniversityBeijing 100191China

neural networks possess formidable representational power,rendering them invaluable in solving complex quantum many-body systems.While they excel at analyzing static solutions,nonequilibrium processes,including critical dynamics during a quantum phase transition,pose a greater challenge for neural networks.To address this,we utilize neural networks and machine learning algorithms to investigate time evolutions,universal statistics,and correlations of topological defects in a one-dimensional transverse-field quantum Ising model.Specifically,our analysis involves computing the energy of the system during a quantum phase transition following a linear quench of the transverse magnetic field strength.The excitation energies satisfy a power-law relation to the quench rate,indicating a proportional relationship between the excitation energy and the kink numbers.Moreover,we establish a universal power-law relationship between the first three cumulants of the kink numbers and the quench rate,indicating a binomial distribution of the kinks.Finally,the normalized kink-kink correlations are also investigated and it is found that the numerical values are consistent with the analytic formula.

关键词： neural networks machine learning transverse-field quantum Ising model kibble-zurek mechanism

Transfer learning and neural networks in predicting quadrupole deformation

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Chinese Physics C 2024年第6期48卷 162-168页

作者：原林李佳星张鸿飞 School of Science Xi'an Polytechnic UniversityXi'an 710048China Engineering Research Center of Flexible Radiation Protection Technology Universities of Shaanxi ProvinceXi'an 710048China Xi'an Key Laboratory of Nuclear Protection Textile Equipment Technology Xi'an 710048China School of Physics Xi'an Jiaotong UniversityXi'an 710049China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China

Accurately determining the quadrupole deformation parameters of atomic nuclei is crucial for understanding their structural and dynamic properties.This study introduces an innovative approach that combines transfer learning techniques with neural networks to predict the quadrupole deformation parameters of even-even nuclei.With the application of this innovative technique,the quadrupole deformation parameters of 2331 even-even nuclei are successfully predicted within the nuclear region defined by proton numbers 8≤Z≤134 and neutron numbers N≥8.Additionally,we discuss the impact of nuclear quadrupole deformation parameters on the capture cross-sections in heavy-ion fusion reactions,reconstructing the capture cross-sections for the reactions48Ca+244Pu and48Ca+248Cm.This research offers new insights into the application of neural networks in nuclear physics and highlights the potential of merging advanced machine learning techniques with both theoretical and experimental data,particularly in fields where experimental data are limited.

关键词： nuclear deformation neural networks transfer learning heavy-ion fusion reactions

Solar Radiation Estimation Based on a New Combined Approach of Artificial neural networks (ANN) and Genetic Algorithms (GA) in South Algeria

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Computers, Materials & Continua 2024年第6期79卷 4725-4740页

作者： Djeldjli Halima Benatiallah Djelloul Ghasri Mehdi Tanougast Camel Benatiallah Ali Benabdelkrim Bouchra Material Sciences Department Faculty of Material SciencesMathematics and Computer ScienceLaboratory of Sustainable Development and Computer Science(LSDCS)University of AdrarAdrar01000Algeria Department of Civil Engineering University of Sistan and BaluchestanZahedan98167-45845Iran Laboratory of Computer Production and Maintenance Engineering(LGIPM)University of LorraineChieulles57070France Laboratory of Energy Environment and Information System(LEEIS) University of AdrarAdrar01000Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

关键词： Solar energy systems genetic algorithm neural networks hybrid adaptive neuro fuzzy inference system solar radiation

Application of Convolutional neural networks in Classification of GBM for Enhanced Prognosis

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Advances in Bioscience and Biotechnology 2024年第2期15卷 91-99页

作者： Rithik Samanthula Herndon USA

The lethal brain tumor “Glioblastoma” has the propensity to grow over time. To improve patient outcomes, it is essential to classify GBM accurately and promptly in order to provide a focused and individualized treatment plan. Despite this, deep learning methods, particularly Convolutional neural networks (CNNs), have demonstrated a high level of accuracy in a myriad of medical image analysis applications as a result of recent technical breakthroughs. The overall aim of the research is to investigate how CNNs can be used to classify GBMs using data from medical imaging, to improve prognosis precision and effectiveness. This research study will demonstrate a suggested methodology that makes use of the CNN architecture and is trained using a database of MRI pictures with this tumor. The constructed model will be assessed based on its overall performance. Extensive experiments and comparisons with conventional machine learning techniques and existing classification methods will also be made. It will be crucial to emphasize the possibility of early and accurate prediction in a clinical workflow because it can have a big impact on treatment planning and patient outcomes. The paramount objective is to not only address the classification challenge but also to outline a clear pathway towards enhancing prognosis precision and treatment effectiveness.

关键词： Glioblastoma Machine Learning Artificial Intelligence neural networks Brain Tumor Cancer Tensorflow Layers Cytoarchitecture Deep Learning Deep neural Network Training Batches