{"title":"Drift-diffusion models for the simulation of a graphene field effect transistor","authors":"G. Nastasi, V. Romano","doi":"10.1186/s13362-022-00120-3","DOIUrl":"https://doi.org/10.1186/s13362-022-00120-3","url":null,"abstract":"","PeriodicalId":44012,"journal":{"name":"Journal of Mathematics in Industry","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65846416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carpio, Ana, Simón, Alejandro, Torres, Alicia, Villa, Luis F.
{"title":"Pattern recognition in data as a diagnosis tool","authors":"Carpio, Ana, Simón, Alejandro, Torres, Alicia, Villa, Luis F.","doi":"10.1186/s13362-022-00119-w","DOIUrl":"https://doi.org/10.1186/s13362-022-00119-w","url":null,"abstract":"Medical data often appear in the form of numerical matrices or sequences. We develop mathematical tools for automatic screening of such data in two medical contexts: diagnosis of systemic lupus erythematosus (SLE) patients and identification of cardiac abnormalities. The idea is first to implement adequate data normalizations and then identify suitable hyperparameters and distances to classify relevant patterns. To this purpose, we discuss the applicability of Plackett-Luce models for rankings to hyperparameter and distance selection. Our tests suggest that, while Hamming distances seem to be well adapted to the study of patterns in matrices representing data from laboratory tests, dynamic time warping distances provide robust tools for the study of cardiac signals. The techniques developed here may set a basis for automatic screening of medical information based on pattern comparison.","PeriodicalId":44012,"journal":{"name":"Journal of Mathematics in Industry","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138526251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Godehardt, A. Moghiseh, Christopher Oetjen, J. Ohser, K. Schladitz
{"title":"An unambiguous cloudiness index for nonwovens","authors":"M. Godehardt, A. Moghiseh, Christopher Oetjen, J. Ohser, K. Schladitz","doi":"10.1186/s13362-022-00124-z","DOIUrl":"https://doi.org/10.1186/s13362-022-00124-z","url":null,"abstract":"","PeriodicalId":44012,"journal":{"name":"Journal of Mathematics in Industry","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44668692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"How deep is your model? Network topology selection from a model validation perspective","authors":"N. Nowaczyk, Jörg Kienitz, S. Acar, Qian Liang","doi":"10.1186/s13362-021-00116-5","DOIUrl":"https://doi.org/10.1186/s13362-021-00116-5","url":null,"abstract":"","PeriodicalId":44012,"journal":{"name":"Journal of Mathematics in Industry","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65846303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A technique for non-intrusive greedy piecewise-rational model reduction of frequency response problems over wide frequency bands.","authors":"Davide Pradovera, Fabio Nobile","doi":"10.1186/s13362-021-00117-4","DOIUrl":"https://doi.org/10.1186/s13362-021-00117-4","url":null,"abstract":"<p><p>In the field of model order reduction for frequency response problems, the minimal rational interpolation (MRI) method has been shown to be quite effective. However, in some cases, numerical instabilities may arise when applying MRI to build a surrogate model over a large frequency range, spanning several orders of magnitude. We propose a strategy to overcome these instabilities, replacing an unstable global MRI surrogate with a union of stable local rational models. The partitioning of the frequency range into local frequency sub-ranges is performed automatically and adaptively, and is complemented by a (greedy) adaptive selection of the sampled frequencies over each sub-range. We verify the effectiveness of our proposed method with two numerical examples.</p>","PeriodicalId":44012,"journal":{"name":"Journal of Mathematics in Industry","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8724177/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39827391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Progress in Industrial Mathematics at ECMI 2021","authors":"Franziska Abend","doi":"10.1007/978-3-319-63082-3","DOIUrl":"https://doi.org/10.1007/978-3-319-63082-3","url":null,"abstract":"","PeriodicalId":44012,"journal":{"name":"Journal of Mathematics in Industry","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79085356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Geometry of Deep Learning","authors":"Jong-Chul Ye","doi":"10.1007/978-981-16-6046-7","DOIUrl":"https://doi.org/10.1007/978-981-16-6046-7","url":null,"abstract":"","PeriodicalId":44012,"journal":{"name":"Journal of Mathematics in Industry","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88452807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Novel Mathematics Inspired by Industrial Challenges","authors":"","doi":"10.1007/978-3-030-96173-2","DOIUrl":"https://doi.org/10.1007/978-3-030-96173-2","url":null,"abstract":"","PeriodicalId":44012,"journal":{"name":"Journal of Mathematics in Industry","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73912807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Moritz Ebeling-Rump, D. Hömberg, Robert Lasarzik, Thomas Petzold
{"title":"Topology optimization subject to additive manufacturing constraints","authors":"Moritz Ebeling-Rump, D. Hömberg, Robert Lasarzik, Thomas Petzold","doi":"10.1186/s13362-021-00115-6","DOIUrl":"https://doi.org/10.1186/s13362-021-00115-6","url":null,"abstract":"","PeriodicalId":44012,"journal":{"name":"Journal of Mathematics in Industry","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65846743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Launay, Hugo, Willot, François, Ryckelynck, David, Besson, Jacques
{"title":"Mechanical assessment of defects in welded joints: morphological classification and data augmentation","authors":"Launay, Hugo, Willot, François, Ryckelynck, David, Besson, Jacques","doi":"10.1186/s13362-021-00114-7","DOIUrl":"https://doi.org/10.1186/s13362-021-00114-7","url":null,"abstract":"We develop a methodology for classifying defects based on their morphology and induced mechanical response. The proposed approach is fairly general and relies on morphological operators (Angulo and Meyer in 9th international symposium on mathematical morphology and its applications to signal and image processing, pp. 226-237, 2009) and spherical harmonic decomposition as a way to characterize the geometry of the pores, and on the Grassman distance evaluated on FFT-based computations (Willot in C. R., Méc. 343(3):232–245, 2015), for the predicted elastic response. We implement and detail our approach on a set of trapped gas pores observed in X-ray tomography of welded joints, that significantly alter the mechanical reliability of these materials (Lacourt et al. in Int. J. Numer. Methods Eng. 121(11):2581–2599, 2020). The space of morphological and mechanical responses is first partitioned into clusters using the “k-medoids” criterion and associated distance functions. Second, we use multiple-layer perceptron neural networks to associate a defect and corresponding morphological representation to its mechanical response. It is found that the method provides accurate mechanical predictions if the training data contains a sufficient number of defects representing each mechanical class. To do so, we supplement the original set of defects by data augmentation techniques. Artificially-generated pore shapes are obtained using the spherical harmonic decomposition and a singular value decomposition performed on the pores signed distance transform. We discuss possible applications of the present method, and how medoids and their associated mechanical response may be used to provide a natural basis for reduced-order models and hyper-reduction techniques, in which the mechanical effects of defects and structures are decorrelated (Ryckelynck et al. in C. R., Méc. 348(10–11):911–935, 2020).","PeriodicalId":44012,"journal":{"name":"Journal of Mathematics in Industry","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2021-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138526225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}