{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T06:25:58Z","timestamp":1783059958100,"version":"3.54.6"},"reference-count":73,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2025YFE0150300"],"award-info":[{"award-number":["2025YFE0150300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai Municipality","doi-asserted-by":"publisher","award":["23ZR1429300"],"award-info":[{"award-number":["23ZR1429300"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U25A20200"],"award-info":[{"award-number":["U25A20200"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.aei.2026.104785","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T06:43:32Z","timestamp":1778481812000},"page":"104785","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["Latent space based inner-outer twin machine: Application to reactor monitoring"],"prefix":"10.1016","volume":"74","author":[{"given":"Rong","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunbing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingren","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengfeng","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4212-9569","authenticated-orcid":false,"given":"Helin","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104785_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.esr.2021.100630","article-title":"Development and outlook of advanced nuclear energy technology","volume":"34","author":"Zhan","year":"2021","journal-title":"Energy Strat. Rev."},{"key":"10.1016\/j.aei.2026.104785_b2","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.egypro.2019.02.193","article-title":"A review on the development of nuclear power reactors","volume":"160","author":"Ho","year":"2019","journal-title":"Energy Procedia"},{"key":"10.1016\/j.aei.2026.104785_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.coche.2022.100878","article-title":"Advanced nuclear energy: the safest and most renewable clean energy","volume":"39","author":"Rehm","year":"2023","journal-title":"Curr. Opin. Chem. Eng."},{"key":"10.1016\/j.aei.2026.104785_b4","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.anucene.2015.01.019","article-title":"Review of multi-physics temporal coupling methods for analysis of nuclear reactors","volume":"84","author":"Zerkak","year":"2015","journal-title":"Ann. Nucl. Energy"},{"key":"10.1016\/j.aei.2026.104785_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.108028","article-title":"A discrete-time Bayesian network approach for reliability analysis of dynamic systems with common cause failures","volume":"216","author":"Guo","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.aei.2026.104785_b6","first-page":"1","article-title":"Modeling and measuring common cause failures in measurement of reliability of nuclear power plant systems","volume":"70","author":"Singh","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"8","key":"10.1016\/j.aei.2026.104785_b7","doi-asserted-by":"crossref","first-page":"2820","DOI":"10.2514\/1.J060131","article-title":"Data-driven aerospace engineering: reframing the industry with machine learning","volume":"59","author":"Brunton","year":"2021","journal-title":"Aiaa J."},{"issue":"4","key":"10.1016\/j.aei.2026.104785_b8","first-page":"14","article-title":"Framework for offline data-driven aircraft fault diagnosis","volume":"21","author":"Kraemer","year":"2024","journal-title":"J. Aerosp. Inf. Syst."},{"issue":"2","key":"10.1016\/j.aei.2026.104785_b9","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.dsm.2024.11.001","article-title":"Challenges and prospects of artificial intelligence in aviation: a bibliometric study","volume":"8","author":"Lopes","year":"2025","journal-title":"Data Sci. Manag."},{"issue":"6","key":"10.1016\/j.aei.2026.104785_b10","doi-asserted-by":"crossref","DOI":"10.1002\/aic.17644","article-title":"Towards artificial intelligence at scale in the chemical industry","volume":"68","author":"Chiang","year":"2022","journal-title":"AIChE J."},{"issue":"7","key":"10.1016\/j.aei.2026.104785_b11","doi-asserted-by":"crossref","first-page":"3197","DOI":"10.1021\/acs.jcim.1c00619","article-title":"Artificial intelligence in chemistry: current trends and future directions","volume":"61","author":"Baum","year":"2021","journal-title":"J. Chem. Inf. Model."},{"issue":"3","key":"10.1016\/j.aei.2026.104785_b12","doi-asserted-by":"crossref","DOI":"10.1063\/5.0147592","article-title":"Machine learning for modern power distribution systems: Progress and perspectives","volume":"15","author":"Markovi\u0107","year":"2023","journal-title":"J. Renew. Sustain. Energy"},{"issue":"1","key":"10.1016\/j.aei.2026.104785_b13","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1146\/annurev-environ-020220-061831","article-title":"Machine learning for sustainable energy systems","volume":"46","author":"Donti","year":"2021","journal-title":"Annu. Rev. Environ. Resour."},{"issue":"6","key":"10.1016\/j.aei.2026.104785_b14","first-page":"1526","article-title":"Nuclear power plant thermocouple sensor-fault detection and classification using deep learning and generalized likelihood ratio test","volume":"64","author":"Mandal","year":"2017","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"10.1016\/j.aei.2026.104785_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.nucengdes.2020.110966","article-title":"Physics-informed reinforcement learning optimization of nuclear assembly design","volume":"372","author":"Radaideh","year":"2021","journal-title":"Nucl. Eng. Des."},{"key":"10.1016\/j.aei.2026.104785_b16","series-title":"A tutorial on Bayesian optimization","author":"Frazier","year":"2018"},{"issue":"7","key":"10.1016\/j.aei.2026.104785_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.net.2025.103527","article-title":"Decision tree based parameter identification and state estimation: Application to reactor operation digital twin","volume":"57","author":"Zhao","year":"2025","journal-title":"Nucl. Eng. Technol."},{"issue":"6","key":"10.1016\/j.aei.2026.104785_b18","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1080\/00295639.2021.2014752","article-title":"Data-enabled physics-informed machine learning for reduced-order modeling digital twin: application to nuclear reactor physics","volume":"196","author":"Gong","year":"2022","journal-title":"Nucl. Sci. Eng."},{"issue":"1","key":"10.1016\/j.aei.2026.104785_b19","doi-asserted-by":"crossref","first-page":"30173","DOI":"10.1038\/s41598-025-13794-7","article-title":"ReactorNet based on machine learning framework to identify control rod position for real time monitoring in PWRs","volume":"15","author":"Omar","year":"2025","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.aei.2026.104785_b20","doi-asserted-by":"crossref","first-page":"5835","DOI":"10.1038\/s41598-024-56388-5","article-title":"Integrating core physics and machine learning for improved parameter prediction in boiling water reactor operations","volume":"14","author":"Oktavian","year":"2024","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.aei.2026.104785_b21","doi-asserted-by":"crossref","first-page":"8904","DOI":"10.1038\/s41467-024-53165-w","article-title":"Generative learning for forecasting the dynamics of high-dimensional complex systems","volume":"15","author":"Gao","year":"2024","journal-title":"Nat. Commun."},{"issue":"10","key":"10.1016\/j.aei.2026.104785_b22","doi-asserted-by":"crossref","DOI":"10.1063\/5.0226562","article-title":"A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics","volume":"36","author":"Zhao","year":"2024","journal-title":"Phys. Fluids"},{"key":"10.1016\/j.aei.2026.104785_b23","doi-asserted-by":"crossref","DOI":"10.1051\/epjn\/2019047","article-title":"3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection","author":"Durrant","year":"2019","journal-title":"EPJ Nucl. Sci. Technol."},{"issue":"12","key":"10.1016\/j.aei.2026.104785_b24","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1080\/00223131.2022.2064358","article-title":"Demonstration of power distribution estimation using ex-core detectors by reactor experiment at UTR-KINKI","volume":"59","author":"Kimura","year":"2022","journal-title":"J. Nucl. Sci. Technol."},{"issue":"12","key":"10.1016\/j.aei.2026.104785_b25","doi-asserted-by":"crossref","first-page":"4983","DOI":"10.1016\/j.net.2024.07.007","article-title":"Continuous mapping of nuclear reactor core power using artificial neural network even in the presence of inactive detectors","volume":"56","author":"Talon","year":"2024","journal-title":"Nucl. Eng. Technol."},{"key":"10.1016\/j.aei.2026.104785_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.nucengdes.2024.113721","article-title":"Using ex-core detectors and deep neural networks for monitoring power distribution in small space reactors","volume":"431","author":"Wang","year":"2025","journal-title":"Nucl. Eng. Des."},{"issue":"8","key":"10.1016\/j.aei.2026.104785_b27","doi-asserted-by":"crossref","first-page":"2240","DOI":"10.1109\/TNS.2018.2854667","article-title":"Compressed sensing artificial neural network for reactor core flux mapping","volume":"65","author":"Bahuguna","year":"2018","journal-title":"IEEE Trans. Nucl. Sci."},{"issue":"1","key":"10.1016\/j.aei.2026.104785_b28","doi-asserted-by":"crossref","first-page":"16840","DOI":"10.1038\/s41598-023-43325-1","article-title":"Physics-informed neural network with transfer learning (TL-PINN) based on domain similarity measure for prediction of nuclear reactor transients","volume":"13","author":"Prantikos","year":"2023","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.aei.2026.104785_b29","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1038\/s41598-024-51984-x","article-title":"Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems","volume":"14","author":"Kobayashi","year":"2024","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.aei.2026.104785_b30","doi-asserted-by":"crossref","first-page":"4400","DOI":"10.1038\/s41598-023-31193-8","article-title":"Anomaly detection using spatial and temporal information in multivariate time series","volume":"13","author":"Tian","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aei.2026.104785_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.nucengdes.2024.112949","article-title":"Graph attention network-based model for multiple fault detection and identification of sensors in nuclear power plant","volume":"419","author":"Liu","year":"2024","journal-title":"Nucl. Eng. Des."},{"key":"10.1016\/j.aei.2026.104785_b32","article-title":"Fault detection for ex-core neutron detectors in nuclear power plants using global-fused dynamic detection model","author":"Lin","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"10.1016\/j.aei.2026.104785_b33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40649-019-0069-y","article-title":"Graph convolutional networks: a comprehensive review","volume":"6","author":"Zhang","year":"2019","journal-title":"Comput. Soc. Netw."},{"key":"10.1016\/j.aei.2026.104785_b34","article-title":"Deep learning-based dual monitoring system for power forecasting and fault detection in nuclear power applications","author":"Lyu","year":"2025","journal-title":"Energy AI"},{"key":"10.1016\/j.aei.2026.104785_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.nucengdes.2023.112502","article-title":"Unsupervised anomaly detection in pressurized water reactor digital twins using autoencoder neural networks","volume":"413","author":"Cancemi","year":"2023","journal-title":"Nucl. Eng. Des."},{"issue":"12","key":"10.1016\/j.aei.2026.104785_b36","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1038\/s42256-024-00938-z","article-title":"Learning spatiotemporal dynamics with a pretrained generative model","volume":"6","author":"Li","year":"2024","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.aei.2026.104785_b37","doi-asserted-by":"crossref","first-page":"33085","DOI":"10.52202\/079017-1042","article-title":"Latent neural operator for solving forward and inverse pde problems","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"3","key":"10.1016\/j.aei.2026.104785_b38","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1038\/s42256-021-00302-5","article-title":"Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators","volume":"3","author":"Lu","year":"2021","journal-title":"Nat. Mach. Intell."},{"issue":"1","key":"10.1016\/j.aei.2026.104785_b39","doi-asserted-by":"crossref","first-page":"5101","DOI":"10.1038\/s41467-024-49411-w","article-title":"Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems","volume":"15","author":"Kontolati","year":"2024","journal-title":"Nat. Commun."},{"issue":"6","key":"10.1016\/j.aei.2026.104785_b40","doi-asserted-by":"crossref","first-page":"3635","DOI":"10.1109\/TNS.2014.2366931","article-title":"Clustering of self powered neutron detectors: combining prompt and slow dynamics","volume":"61","author":"Razak","year":"2014","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"10.1016\/j.aei.2026.104785_b41","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.pnucene.2017.04.017","article-title":"Application of data reconciliation for fault detection and isolation of in-core self-powered neutron detectors using iterative principal component test","volume":"100","author":"Yellapu","year":"2017","journal-title":"Prog. Nucl. Energy"},{"issue":"5","key":"10.1016\/j.aei.2026.104785_b42","doi-asserted-by":"crossref","first-page":"A2403","DOI":"10.1137\/24M1670421","article-title":"Coupled input-output dimension reduction: application to goal-oriented Bayesian experimental design and global sensitivity analysis","volume":"47","author":"Chen","year":"2025","journal-title":"SIAM J. Sci. Comput."},{"key":"10.1016\/j.aei.2026.104785_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2026.117082","article-title":"Multi-method fault detection considering uncertainty through MC dropout for enhanced voting","volume":"356","author":"Alexander Nissen S\u00f8ndergaard","year":"2026","journal-title":"Energy Build."},{"key":"10.1016\/j.aei.2026.104785_b44","first-page":"2","article-title":"Dropout as a Bayesian approximation: Insights and applications","volume":"vol. 1","author":"Gal","year":"2015"},{"key":"10.1016\/j.aei.2026.104785_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.anucene.2026.112479","article-title":"Machine learning-based multi-model detector anomaly detection: Application to nuclear reactor core","author":"Jiang","year":"2026","journal-title":"Ann. Nucl. Energy"},{"key":"10.1016\/j.aei.2026.104785_b46","series-title":"Kan: Kolmogorov-arnold networks","author":"Liu","year":"2024"},{"key":"10.1016\/j.aei.2026.104785_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2025.100473","article-title":"Comparison of Kolmogorov\u2013arnold networks and multi-layer perceptron for modelling and optimisation analysis of energy systems","volume":"20","author":"Ansar","year":"2025","journal-title":"Energy AI"},{"issue":"4","key":"10.1016\/j.aei.2026.104785_b48","doi-asserted-by":"crossref","DOI":"10.3390\/s18040967","article-title":"Optimize the coverage probability of prediction interval for anomaly detection of sensor-based monitoring series","volume":"18","author":"Pang","year":"2018","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.aei.2026.104785_b49","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1198\/000313008X304062","article-title":"Choosing a coverage probability for prediction intervals","volume":"62","author":"Landon","year":"2008","journal-title":"Amer. Statist."},{"key":"10.1016\/j.aei.2026.104785_b50","series-title":"International Conference on Machine Learning","first-page":"1321","article-title":"On calibration of modern neural networks","author":"Guo","year":"2017"},{"key":"10.1016\/j.aei.2026.104785_b51","series-title":"International Conference on Machine Learning","first-page":"2796","article-title":"Accurate uncertainties for deep learning using calibrated regression","author":"Kuleshov","year":"2018"},{"key":"10.1016\/j.aei.2026.104785_b52","series-title":"Quantifying Reactor Safety Margins: Application of Code Scaling, Applicability, and Uncertainty Evaluation Methodology to a Large-Break, Loss-Of-Coolant Accident","author":"Boyack","year":"1989"},{"key":"10.1016\/j.aei.2026.104785_b53","first-page":"157","article-title":"Best-estimate calculations of emergency core cooling system performance","volume":"1","author":"Commission","year":"1989","journal-title":"Regul. Guid."},{"key":"10.1016\/j.aei.2026.104785_b54","article-title":"Criteria for accident monitoring instrumentation for nuclear power plants","volume":"1","author":"Commission","year":"2006","journal-title":"Regul. Guid."},{"key":"10.1016\/j.aei.2026.104785_b55","series-title":"Technical and Economic Aspects of Load Following with Nuclear Power Plants","author":"OECD","year":"2021"},{"key":"10.1016\/j.aei.2026.104785_b56","series-title":"The Physics of Nuclear Reactors","author":"Marguet","year":"2018"},{"key":"10.1016\/j.aei.2026.104785_b57","series-title":"Guide for Validation of Nuclear Criticality Safety Calculational Methodology","author":"Dean","year":"2001"},{"key":"10.1016\/j.aei.2026.104785_b58","series-title":"Deterministic Safety Analysis for Nuclear Power Plants. IAEA Specific Safety Guide","author":"D\u2019Auria","year":"2009"},{"issue":"7","key":"10.1016\/j.aei.2026.104785_b59","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1016\/j.net.2019.05.015","article-title":"Development and validation of reactor nuclear design code CORCA-3D","volume":"51","author":"An","year":"2019","journal-title":"Nucl. Eng. Technol."},{"key":"10.1016\/j.aei.2026.104785_b60","doi-asserted-by":"crossref","unstructured":"H. Hashemian, B. Shumaker, G. Morton, Online monitoring technology to extend calibration intervals of nuclear plant pressure transmitters. http:\/\/dx.doi.org\/10.13182\/t124-34344.","DOI":"10.13182\/T124-34344"},{"key":"10.1016\/j.aei.2026.104785_b61","doi-asserted-by":"crossref","DOI":"10.1016\/j.anucene.2021.108473","article-title":"Continuous online monitoring in pressurized water reactors during flexible operation using PLSR-based technique\u2013case study: Load following test","volume":"161","author":"Elsamahy","year":"2021","journal-title":"Ann. Nucl. Energy"},{"key":"10.1016\/j.aei.2026.104785_b62","series-title":"Online monitoring technology to extend calibration intervals of nuclear plant pressure transmitters","author":"Analysis and Measurement Services Corporation","year":"2020"},{"issue":"1","key":"10.1016\/j.aei.2026.104785_b63","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1002\/oca.979","article-title":"Fault detection for linear stochastic systems with sensor stuck faults","volume":"33","author":"Li","year":"2012","journal-title":"Optim. Control. Appl. Methods"},{"issue":"8","key":"10.1016\/j.aei.2026.104785_b64","first-page":"2008","article-title":"Multi-sensor fault detection and isolation algorithm","volume":"61","author":"Chen","year":"2010","journal-title":"CIESC J."},{"key":"10.1016\/j.aei.2026.104785_b65","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102452","article-title":"A distributed state and fault estimation scheme for state-saturated systems with quantized measurements over sensor networks","volume":"110","author":"Huang","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.aei.2026.104785_b66","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.automatica.2018.07.027","article-title":"Joint state and fault estimation for time-varying nonlinear systems with randomly occurring faults and sensor saturations","volume":"97","author":"Dong","year":"2018","journal-title":"Automatica"},{"issue":"1","key":"10.1016\/j.aei.2026.104785_b67","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/J.ENG.2016.01.017","article-title":"HPR1000: advanced pressurized water reactor with active and passive safety","volume":"2","author":"Xing","year":"2016","journal-title":"Engineering"},{"key":"10.1016\/j.aei.2026.104785_b68","series-title":"Proceedings of the International Conference on Physics of Reactors (PHYSOR2014)","first-page":"6","article-title":"Development and preliminary verification of the PWR on-line core monitoring software system. SOPHORA","author":"Wenhuai","year":"2015"},{"issue":"1","key":"10.1016\/j.aei.2026.104785_b69","doi-asserted-by":"crossref","first-page":"4950","DOI":"10.1038\/s41467-018-07210-0","article-title":"Deep learning for universal linear embeddings of nonlinear dynamics","volume":"9","author":"Lusch","year":"2018","journal-title":"Nat. Commun."},{"issue":"45","key":"10.1016\/j.aei.2026.104785_b70","doi-asserted-by":"crossref","first-page":"22445","DOI":"10.1073\/pnas.1906995116","article-title":"Data-driven discovery of coordinates and governing equations","volume":"116","author":"Champion","year":"2019","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.aei.2026.104785_b71","doi-asserted-by":"crossref","unstructured":"M. Lyu, H. Gong, Z. Chen, J. Wang, M. Zhong, Z. Wang, Q. Li, Z. Pan, Dual Monitoring System for Power Prediction and Fault Diagnosis with Mh-Sa Lstm. Available At SSRN 5132765. http:\/\/dx.doi.org\/10.2139\/ssrn.5132765.","DOI":"10.2139\/ssrn.5132765"},{"key":"10.1016\/j.aei.2026.104785_b72","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111427","article-title":"Can foundation language models predict fluid dynamics?","volume":"158","author":"Wang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104785_b73","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2025.100555","article-title":"Automating Monte Carlo simulations in nuclear engineering with domain knowledge-embedded large language model agents","author":"Ndum","year":"2025","journal-title":"Energy AI"}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626004775?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626004775?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T06:10:12Z","timestamp":1783059012000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626004775"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":73,"alternative-id":["S1474034626004775"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104785","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Latent space based inner-outer twin machine: Application to reactor monitoring","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104785","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104785"}}