{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:57Z","timestamp":1740108057657,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"35","license":[{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100006192","name":"Advanced Scientific Computing Research","doi-asserted-by":"publisher","award":["DE-AC02-06CH11357"],"award-info":[{"award-number":["DE-AC02-06CH11357"]}],"id":[{"id":"10.13039\/100006192","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006151","name":"Basic Energy Sciences","doi-asserted-by":"publisher","award":["DE-AC02-06CH11357"],"award-info":[{"award-number":["DE-AC02-06CH11357"]}],"id":[{"id":"10.13039\/100006151","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s00521-024-10415-8","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T05:01:58Z","timestamp":1726722118000},"page":"22335-22346","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated defect identification in coherent diffraction imaging with smart continual learning"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9360-2489","authenticated-orcid":false,"given":"Orcun","family":"Yildiz","sequence":"first","affiliation":[]},{"given":"Krishnan","family":"Raghavan","sequence":"additional","affiliation":[]},{"given":"Henry","family":"Chan","sequence":"additional","affiliation":[]},{"given":"Mathew J.","family":"Cherukara","sequence":"additional","affiliation":[]},{"given":"Prasanna","family":"Balaprakash","sequence":"additional","affiliation":[]},{"given":"Subramanian","family":"Sankaranarayanan","sequence":"additional","affiliation":[]},{"given":"Tom","family":"Peterka","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"issue":"1","key":"10415_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41524-022-00803-w","volume":"8","author":"Y Yao","year":"2022","unstructured":"Yao Y, Chan H, Sankaranarayanan S, Balaprakash P, Harder RJ, Cherukara MJ (2022) Autophasenn: unsupervised physics-aware deep learning of 3d nanoscale bragg coherent diffraction imaging. npj Comput Mater 8(1):1\u20138","journal-title":"npj Comput Mater"},{"issue":"4","key":"10415_CR2","doi-asserted-by":"publisher","DOI":"10.1063\/5.0013065","volume":"117","author":"MJ Cherukara","year":"2020","unstructured":"Cherukara MJ, Zhou T, Nashed Y, Enfedaque P, Hexemer A, Harder RJ, Holt MV (2020) Ai-enabled high-resolution scanning coherent diffraction imaging. Appl Phys Lett 117(4):044103","journal-title":"Appl Phys Lett"},{"issue":"4","key":"10415_CR3","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1080\/08940886.2022.2112500","volume":"35","author":"C Benmore","year":"2022","unstructured":"Benmore C, Bicer T, Chan MK, Di Z, G\u00fcrsoy DA, Hwang I, Kuklev N, Lin D, Liu Z, Lobach I et al (2022) Advancing ai\/ml at the advanced photon source. Synchrotron Radiat News 35(4):28\u201335","journal-title":"Synchrotron Radiat News"},{"issue":"1","key":"10415_CR4","doi-asserted-by":"publisher","first-page":"015114","DOI":"10.1063\/1.5017596","volume":"8","author":"A Ulvestad","year":"2018","unstructured":"Ulvestad A, Menickelly M, Wild S (2018) Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation. AIP Adv 8(1):015114","journal-title":"AIP Adv"},{"issue":"1","key":"10415_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-09582-7","volume":"7","author":"A Ulvestad","year":"2017","unstructured":"Ulvestad A, Nashed Y, Beutier G, Verdier M, Hruszkewycz S, Dupraz M (2017) Identifying defects with guided algorithms in bragg coherent diffractive imaging. Sci Rep 7(1):1\u20139","journal-title":"Sci Rep"},{"issue":"1","key":"10415_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41524-021-00583-9","volume":"7","author":"B Lim","year":"2021","unstructured":"Lim B, Bellec E, Dupraz M, Leake S, Resta A, Coati A, Sprung M, Almog E, Rabkin E, Schulli T et al (2021) A convolutional neural network for defect classification in bragg coherent x-ray diffraction. npj Comput Mater 7(1):1\u20138","journal-title":"npj Comput Mater"},{"issue":"2","key":"10415_CR7","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1557\/s43577-022-00342-1","volume":"48","author":"W Judge","year":"2023","unstructured":"Judge W, Chan H, Sankaranarayanan S, Harder RJ, Cabana J, Cherukara MJ (2023) Defect identification in simulated bragg coherent diffraction imaging by automated ai. MRS Bull 48(2):124\u2013133","journal-title":"MRS Bull"},{"key":"10415_CR8","doi-asserted-by":"crossref","unstructured":"Babu AV, Zhou T, Kandel S, Bicer T, Liu Z, Judge W, Ching DJ, Jiang Y, Veseli S, Henke S, et\u00a0al. (2022) Deep learning at the edge enables real-time streaming ptychographic imaging. arXiv preprint arXiv:2209.09408","DOI":"10.1038\/s41467-023-41496-z"},{"key":"10415_CR9","doi-asserted-by":"crossref","unstructured":"Babu AV, Bicer T, Kandel S, Zhou T, Ching DJ, Henke S, Veseli S, Chard R, Miceli A, Cherukara MJ (2023) Ai-assisted automated workflow for real-time x-ray ptychography data analysis via federated resources. arXiv preprint arXiv:2304.04297","DOI":"10.2352\/EI.2023.35.11.HPCI-232"},{"issue":"1","key":"10415_CR10","doi-asserted-by":"publisher","first-page":"011301","DOI":"10.1063\/1.2403783","volume":"78","author":"S Marchesini","year":"2007","unstructured":"Marchesini S (2007) Invited article: a unified evaluation of iterative projection algorithms for phase retrieval. Rev Sci Instrum 78(1):011301","journal-title":"Rev Sci Instrum"},{"issue":"1","key":"10415_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-34525-1","volume":"8","author":"MJ Cherukara","year":"2018","unstructured":"Cherukara MJ, Nashed YS, Harder RJ (2018) Real-time coherent diffraction inversion using deep generative networks. Sci Rep 8(1):1\u20138","journal-title":"Sci Rep"},{"issue":"18","key":"10415_CR12","doi-asserted-by":"publisher","first-page":"184901","DOI":"10.1063\/5.0014725","volume":"128","author":"A Scheinker","year":"2020","unstructured":"Scheinker A, Pokharel R (2020) Adaptive 3d convolutional neural network-based reconstruction method for 3d coherent diffraction imaging. J Appl Phys 128(18):184901","journal-title":"J Appl Phys"},{"issue":"1","key":"10415_CR13","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1107\/S2052252520013780","volume":"8","author":"L Wu","year":"2021","unstructured":"Wu L, Juhas P, Yoo S, Robinson I (2021) Complex imaging of phase domains by deep neural networks. IUCrJ 8(1):12\u201321","journal-title":"IUCrJ"},{"issue":"Pt 1","key":"10415_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1107\/S2052252520016590","volume":"8","author":"R Harder","year":"2021","unstructured":"Harder R (2021) Deep neural networks in real-time coherent diffraction imaging. IUCrJ 8(Pt 1):1","journal-title":"IUCrJ"},{"issue":"2","key":"10415_CR15","doi-asserted-by":"publisher","first-page":"021407","DOI":"10.1063\/5.0031486","volume":"8","author":"H Chan","year":"2021","unstructured":"Chan H, Nashed YS, Kandel S, Hruszkewycz SO, Sankaranarayanan SK, Harder RJ, Cherukara MJ (2021) Rapid 3d nanoscale coherent imaging via physics-aware deep learning. Appl Phys Rev 8(2):021407","journal-title":"Appl Phys Rev"},{"issue":"5","key":"10415_CR16","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.future.2008.06.012","volume":"25","author":"E Deelman","year":"2009","unstructured":"Deelman E, Gannon D, Shields M, Taylor I (2009) Workflows and e-science: an overview of workflow system features and capabilities. Futur Gener Comput Syst 25(5):528\u2013540","journal-title":"Futur Gener Comput Syst"},{"issue":"4","key":"10415_CR17","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1109\/MCSE.2019.2919688","volume":"21","author":"I Altintas","year":"2019","unstructured":"Altintas I, Purawat S, Crawl D, Singh A, Marcus K (2019) Toward a methodology and framework for workflow-driven team science. Comput Sci Eng 21(4):37\u201348","journal-title":"Comput Sci Eng"},{"key":"10415_CR18","doi-asserted-by":"crossref","unstructured":"Ayachit U, Bauer A, Geveci B, O\u2019Leary P, Moreland K, Fabian N, Mauldin J (2015) In Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ACM), pp. 25\u201329","DOI":"10.1145\/2828612.2828624"},{"key":"10415_CR19","unstructured":"Kuhlen T, Pajarola R, Zhou K (2011) Proceedings of the 11th Eurographics Conference on Parallel Graphics and Visualization (EGPGV)"},{"key":"10415_CR20","doi-asserted-by":"crossref","unstructured":"Boyuka DA, Lakshminarasimham S, Zou X, Gong Z, Jenkins J, Schendel ER, Podhorszki N, Liu Q, Klasky S, Samatova NF (2014) In 2014 14th IEEE\/ACM International Symposium on Cluster, Cloud and Grid Computing (IEEE), pp. 256\u2013266","DOI":"10.1109\/CCGrid.2014.73"},{"key":"10415_CR21","doi-asserted-by":"crossref","unstructured":"Ayachit U, Whitlock B, Wolf M, Loring B, Geveci B, Lonie D, Bethel E (2016) Proceedings of the 2nd Workshop on In Situ Infrastructures for Enabling Extreme-scale Analysis and Visualization (IEEE Press), pp. 40\u201344","DOI":"10.1109\/ISAV.2016.013"},{"issue":"3","key":"10415_CR22","first-page":"15","volume":"3","author":"M Dorier","year":"2016","unstructured":"Dorier M, Antoniu G, Cappello F, Snir M, Sisneros R, Yildiz O, Ibrahim S, Peterka T, Orf L (2016) Damaris: addressing performance variability in data management for post-petascale simulations. ACM Trans Parallel Comput (TOPC) 3(3):15","journal-title":"ACM Trans Parallel Comput (TOPC)"},{"key":"10415_CR23","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-030-81627-8_7","volume-title":"Situ Visualization for Computational Science","author":"O Yildiz","year":"2022","unstructured":"Yildiz O, Dreher M, Peterka T (2022) Situ Visualization for Computational Science. Springer, Cham, pp 137\u2013158"},{"issue":"13","key":"10415_CR24","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, Milan K, Quan J, Ramalho T, Grabska-Barwinska A et al (2017) Overcoming catastrophic forgetting in neural networks. Proc Natl Acad Sci 114(13):3521\u20133526","journal-title":"Proc Natl Acad Sci"},{"key":"10415_CR25","unstructured":"Van\u00a0de Ven GM, Tolias AS (2019) Three scenarios for continual learning. arXiv preprint arXiv:1904.07734"},{"key":"10415_CR26","first-page":"17284","volume":"34","author":"K Raghavan","year":"2021","unstructured":"Raghavan K, Balaprakash P (2021) Formalizing the generalization-forgetting trade-off in continual learning. Adv Neural Inf Process Syst 34:17284\u201317297","journal-title":"Adv Neural Inf Process Syst"},{"key":"10415_CR27","unstructured":"Krishnan R, Balaprakash P (2020) Meta continual learning via dynamic programming. arXiv preprint arXiv:2008.02219"},{"key":"10415_CR28","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.cpc.2015.07.012","volume":"197","author":"P Hirel","year":"2015","unstructured":"Hirel P (2015) Atomsk: a tool for manipulating and converting atomic data files. Comput Phys Commun 197:212\u2013219. https:\/\/doi.org\/10.1016\/j.cpc.2015.07.012","journal-title":"Comput Phys Commun"},{"key":"10415_CR29","first-page":"43","volume":"18","author":"S Plimpton","year":"2007","unstructured":"Plimpton S, Crozier P, Thompson A (2007) Lammps-large-scale atomic\/molecular massively parallel simulator. Sandia Nation Lab 18:43","journal-title":"Sandia Nation Lab"},{"issue":"5","key":"10415_CR30","doi-asserted-by":"publisher","first-page":"1404","DOI":"10.1107\/S1600576720010985","volume":"53","author":"V Favre-Nicolin","year":"2020","unstructured":"Favre-Nicolin V, Girard G, Leake S, Carnis J, Chushkin Y, Kieffer J, Paleo P, Richard MI (2020) Pynx: high-performance computing toolkit for coherent x-ray imaging based on operators. J Appl Crystallogr 53(5):1404\u20131413","journal-title":"J Appl Crystallogr"},{"issue":"3","key":"10415_CR31","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1107\/S0021889811009009","volume":"44","author":"V Favre-Nicolin","year":"2011","unstructured":"Favre-Nicolin V, Coraux J, Richard MI, Renevier H (2011) Fast computation of scattering maps of nanostructures using graphical processing units. J Appl Crystallogr 44(3):635\u2013640","journal-title":"J Appl Crystallogr"},{"key":"10415_CR32","first-page":"8024","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L et al (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:8024\u20138035","journal-title":"Adv Neural Inf Process Syst"},{"key":"10415_CR33","first-page":"14435","volume":"33","author":"L Zhao","year":"2020","unstructured":"Zhao L, Liu T, Peng X, Metaxas D (2020) Maximum-entropy adversarial data augmentation for improved generalization and robustness. Adv Neural Inf Process Syst 33:14435\u201314447","journal-title":"Adv Neural Inf Process Syst"},{"key":"10415_CR34","unstructured":"Fornek TE (2017) Advanced photon source upgrade project preliminary design report (No. APSU-2.01-RPT-002). Tech. rep., Argonne National Laboratory (ANL)(United States). Funding organisation: USDOE Office of Science-Office of Basic Energy Sciences, United States"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10415-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10415-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10415-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T12:04:40Z","timestamp":1732536280000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10415-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,19]]},"references-count":34,"journal-issue":{"issue":"35","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["10415"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10415-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,9,19]]},"assertion":[{"value":"30 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}