{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T22:40:05Z","timestamp":1749422405815,"version":"3.41.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030779764"},{"type":"electronic","value":"9783030779771"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-77977-1_44","type":"book-chapter","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T07:07:25Z","timestamp":1623222445000},"page":"549-561","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Deep Learning Approach for Polycrystalline Microstructure-Statistical Property Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3358-284X","authenticated-orcid":false,"given":"Jos\u00e9 Pablo","family":"Quesada-Molina","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5111-9800","authenticated-orcid":false,"given":"Stefano","family":"Mariani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"key":"44_CR1","volume-title":"MEMS and Microsystems: Design, Manufacture, and Nanoscale Engineering","author":"TR Hsu","year":"2008","unstructured":"Hsu, T.R.: MEMS and Microsystems: Design, Manufacture, and Nanoscale Engineering. John Wiley & Sons, Hoboken, NJ, USA (2008)"},{"key":"44_CR2","volume-title":"Reliability of MEMS Testing of Materials and Devices","author":"O Brand","year":"2013","unstructured":"Brand, O., Fedder, G.K., Hierold, C., Korvink, J.G., Tabata, O., Tsuchiya, T.: Reliability of MEMS Testing of Materials and Devices. John Wiley & Sons, Hoboken, NJ, USA (2013)"},{"key":"44_CR3","doi-asserted-by":"publisher","DOI":"10.1002\/9781119053828","volume-title":"Mechanics of Microsystems","author":"A Corigliano","year":"2018","unstructured":"Corigliano, A., Ardito, R., Comi, C., Frangi, A., Ghisi, A., Mariani, S.: Mechanics of Microsystems. John Wiley & Sons, Hoboken, NJ, USA (2018)"},{"key":"44_CR4","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1109\/JMEMS.2006.876779","volume":"15","author":"MS Weinberg","year":"2006","unstructured":"Weinberg, M.S., Kourepenis, A.: Error sources in in-plane silicon tuning-fork MEMS gyroscopes. J. Microelectromech. Syst. 15, 479\u2013491 (2006). https:\/\/doi.org\/10.1109\/JMEMS.2006.876779","journal-title":"J. Microelectromech. Syst."},{"key":"44_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1088\/0960-1317\/26\/6\/063001","volume":"26","author":"M De Laat","year":"2016","unstructured":"De Laat, M., P\u00e9rez Garza, H., Herder, J., Ghatkesar, M.: A review on in situ stiffness adjustment methods in MEMS. J. Micromech. Microeng. 26, 1\u201321 (2016). https:\/\/doi.org\/10.1088\/0960-1317\/26\/6\/063001","journal-title":"J. Micromech. Microeng."},{"key":"44_CR6","first-page":"349","volume":"61","author":"T Uhl","year":"2009","unstructured":"Uhl, T., Martowicz, A., Codreanu, I., Klepka, A.: Analysis of uncertainties in MEMS and their influence on dynamic properties. Arch. Mech. 61, 349\u2013370 (2009)","journal-title":"Arch. Mech."},{"key":"44_CR7","doi-asserted-by":"publisher","first-page":"36","DOI":"10.3390\/act8020036","volume":"8","author":"M Bagherinia","year":"2019","unstructured":"Bagherinia, M., Mariani, S.: Stochastic effects on the dynamics of the resonant structure of a Lorentz Force MEMS magnetometer. Actuators. 8, 36 (2019). https:\/\/doi.org\/10.3390\/act8020036","journal-title":"Actuators."},{"key":"44_CR8","doi-asserted-by":"publisher","first-page":"248","DOI":"10.3390\/mi8080248","volume":"8","author":"R Mirzazadeh","year":"2017","unstructured":"Mirzazadeh, R., Mariani, S.: Uncertainty quantification of microstructure-governed properties of polysilicon MEMS. Micromachines. 8, 248 (2017). https:\/\/doi.org\/10.3390\/mi8080248","journal-title":"Micromachines."},{"key":"44_CR9","doi-asserted-by":"publisher","first-page":"53","DOI":"10.3390\/mi9020053","volume":"9","author":"R Mirzazadeh","year":"2018","unstructured":"Mirzazadeh, R., Ghisi, A., Mariani, S.: Statistical investigation of the mechanical and geometrical properties of polysilicon films through on-chip tests. Micromachines. 9, 53 (2018). https:\/\/doi.org\/10.3390\/mi9020053","journal-title":"Micromachines."},{"key":"44_CR10","doi-asserted-by":"publisher","first-page":"84","DOI":"10.2174\/1876402911666181204122855","volume":"10","author":"S Mariani","year":"2018","unstructured":"Mariani, S., Ghisi, A., Mirzazadeh, R., Eftekhar Azam, S.: On-chip testing: a miniaturized lab to assess sub-micron uncertainties in polysilicon MEMS. Micro Nanosyst. 10, 84\u201393 (2018). https:\/\/doi.org\/10.2174\/1876402911666181204122855","journal-title":"Micro Nanosyst."},{"key":"44_CR11","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.3390\/s18041243","volume":"18","author":"R Mirzazadeh","year":"2018","unstructured":"Mirzazadeh, R., Eftekhar Azam, S., Mariani, S.: Mechanical characterization of polysilicon MEMS: a hybrid TMCMC\/POD-kriging approach. Sensors. 18, 1243 (2018). https:\/\/doi.org\/10.3390\/s18041243","journal-title":"Sensors."},{"key":"44_CR12","doi-asserted-by":"publisher","first-page":"3256","DOI":"10.3390\/s19153256","volume":"19","author":"A Ghisi","year":"2019","unstructured":"Ghisi, A., Mariani, S.: Effect of imperfections due to material heterogeneity on the offset of polysilicon MEMS structures. Sensors. 19, 3256 (2019). https:\/\/doi.org\/10.3390\/s19153256","journal-title":"Sensors."},{"key":"44_CR13","doi-asserted-by":"publisher","first-page":"4972","DOI":"10.3390\/s110504972","volume":"11","author":"S Mariani","year":"2011","unstructured":"Mariani, S., Ghisi, A., Corigliano, A., Martini, R., Simoni, B.: Two-scale simulation of drop-induced failure of polysilicon MEMS sensors. Sensors. 11, 4972\u20134989 (2011). https:\/\/doi.org\/10.3390\/s110504972","journal-title":"Sensors."},{"key":"44_CR14","doi-asserted-by":"publisher","first-page":"13985","DOI":"10.3390\/s121013985","volume":"12","author":"A Ghisi","year":"2012","unstructured":"Ghisi, A., Mariani, S., Corigliano, A., Zerbini, S.: Physically-based reduced order modelling of a uni-axial polysilicon MEMS accelerometer. Sensors. 12, 13985\u201314003 (2012). https:\/\/doi.org\/10.3390\/s121013985","journal-title":"Sensors."},{"key":"44_CR15","doi-asserted-by":"publisher","unstructured":"Quesada-Molina, J.P., Rosafalco, L., Mariani, S.: Stochastic mechanical characterization of polysilicon MEMS: a deep learning approach. In: Proceedings of 6th International Electronic Conference on Sensors and Applications, vol. 42, p. 8 (2020). https:\/\/doi.org\/10.3390\/ecsa-6-06574","DOI":"10.3390\/ecsa-6-06574"},{"key":"44_CR16","doi-asserted-by":"publisher","unstructured":"Quesada-Molina, J.P., Rosafalco, L., Mariani, S.: Mechanical characterization of polysilicon MEMS devices: a stochastic, deep learning-based approach. In: 2020 21st International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE), pp. 1\u20138, IEEE Press. New York (2020). https:\/\/doi.org\/10.1109\/EuroSimE48426.2020.9152690","DOI":"10.1109\/EuroSimE48426.2020.9152690"},{"key":"44_CR17","doi-asserted-by":"publisher","first-page":"110","DOI":"10.3389\/fmats.2019.00110","volume":"6","author":"FE Bock","year":"2019","unstructured":"Bock, F.E., Aydin, R.C., Cyron, C.J., Huber, N., Kalidindi, S.R., Klusemann, B.: A review of the application of machine learning and data mining approaches in continuum materials mechanics. Front. Mater. 6, 110 (2019). https:\/\/doi.org\/10.3389\/fmats.2019.00110","journal-title":"Front. Mater."},{"key":"44_CR18","doi-asserted-by":"publisher","unstructured":"Cang, R., Ren, M.Y.: Deep network-based feature extraction and reconstruction of complex material microstructures. In: Proceedings of the ASME 2016 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, pp. 1\u201310 (2016). https:\/\/doi.org\/10.1115\/DETC2016-59404","DOI":"10.1115\/DETC2016-59404"},{"key":"44_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1103\/PhysRevE.96.052111","volume":"96","author":"N Lubbers","year":"2017","unstructured":"Lubbers, N., Lookman, T., Barros, K.: Inferring low-dimensional microstructure representations using convolutional neural networks. Phys. Rev. E 96, 1\u201314 (2017). https:\/\/doi.org\/10.1103\/PhysRevE.96.052111","journal-title":"Phys. Rev. E"},{"key":"44_CR20","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.commatsci.2018.05.014","volume":"151","author":"Z Yang","year":"2018","unstructured":"Yang, Z., et al.: Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets. Comput. Mater. Sci. 151, 278\u2013287 (2018). https:\/\/doi.org\/10.1016\/j.commatsci.2018.05.014","journal-title":"Comput. Mater. Sci."},{"key":"44_CR21","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.actamat.2017.11.053","volume":"146","author":"A Cecen","year":"2018","unstructured":"Cecen, A., Dai, H., Yabansu, Y.C., Kalidindi, S.R., Song, L.: Material structure-property linkages using three-dimensional convolutional neural networks. Acta Mater. 146, 76\u201384 (2018). https:\/\/doi.org\/10.1016\/j.actamat.2017.11.053","journal-title":"Acta Mater."},{"key":"44_CR22","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.commatsci.2018.03.074","volume":"150","author":"R Cang","year":"2018","unstructured":"Cang, R., Li, H., Yao, H., Jiao, Y., Ren, Y.: Improving direct physical properties prediction of heterogeneous materials from imaging data via convolutional neural network and a morphology-aware generative model. Comput. Mater. Sci. 150, 212\u2013221 (2018). https:\/\/doi.org\/10.1016\/j.commatsci.2018.03.074","journal-title":"Comput. Mater. Sci."},{"key":"44_CR23","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778. IEEE Press, New York (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"44_CR24","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261\u20132269. IEEE Press, New York (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"44_CR25","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.probengmech.2005.07.007","volume":"21","author":"M Otoja-Starzewski","year":"2006","unstructured":"Otoja-Starzewski, M.: Material spatial randomness: from statistical to representative volume element. Probab. Eng. Mech. 21, 112\u2013132 (2006). https:\/\/doi.org\/10.1016\/j.probengmech.2005.07.007","journal-title":"Probab. Eng. Mech."},{"key":"44_CR26","doi-asserted-by":"publisher","first-page":"3647","DOI":"10.1016\/S0020-7683(03)00143-4","volume":"40","author":"T Kanit","year":"2003","unstructured":"Kanit, T., Forest, S., Galliet, I., Mounoury, V., Jeulin, D.: Determination of the size of the representative volume element for random composites: statistical and numerical approach. Int. J. Solids Struct. 40, 3647\u20133679 (2003). https:\/\/doi.org\/10.1016\/S0020-7683(03)00143-4","journal-title":"Int. J. Solids Struct."},{"key":"44_CR27","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1615\/IntJMultCompEng.v9.i3.50","volume":"9","author":"S Mariani","year":"2011","unstructured":"Mariani, S., Martini, R., Ghisi, A., Corigliano, A., Beghi, M.: Overall elastic properties of polysilicon films: a statistical investigation of the effects of polycrystal morphology. J. Multiscale Comput. Eng. 9, 327\u2013346 (2011). https:\/\/doi.org\/10.1615\/IntJMultCompEng.v9.i3.50","journal-title":"J. Multiscale Comput. Eng."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-77977-1_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T22:02:55Z","timestamp":1749420175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-77977-1_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030779764","9783030779771"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-77977-1_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Krakow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"156","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.9","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"212 full and 43 short papers were selected from 479 submissions to the workshops\/ thematic tracks. The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}