[1]	TANKARD C. Big data security[J]. Network Security, 2012(7): 5-8. 本文引用 [1]

[2]	MAYER-SCHÖNBERGER V, CUKIER K. Big data: a revo-lution that will transform how we live, work, and think[M]. Boston: Houghton Mifflin Harcourt, 2013. 本文引用 [1]

[3]	Ocean circulation is changing, and we need to know why[J]. Nature, 2018, 556(7700): 149. 本文引用 [1]

[4]	ANDO K, LIN X P, VILLANOY C, et al. Half-century of scientific advancements since the cooperative study of the Kuroshio and adjacent regions (CSK) programme—Need for a new Kuroshio research[J]. Progress in Oceanography, 2021, 193: 102513. 本文引用 [1]

[5]	钱程程, 陈戈. 海洋大数据科学发展现状与展望[J]. 中国科学院院刊, 2018, 33(8):884-891. QIAN C C, CHEN G. Big data science for ocean: Present and future[J]. Bulletin of Chinese Academy of Sciences, 2018, 33(8): 884-891. 本文引用 [1]

[6]	SCHER S, MESSORI G. Weather and climate forecasting with neural networks: Using general circulation models (GCMs) with different complexity as a study ground[J]. Geoscientific Model Development, 2019, 12(7): 2797-2809. 本文引用 [1]

[7]	CHEN G, HUANG B X, CHEN X Y, et al. Deep blue AI: A new bridge from data to knowledge for the ocean science[J]. Deep Sea Research Part I: Oceanographic Research Papers, 2022, 190: 103886. 本文引用 [1]

[8]	QIAN C C, HUANG B X, YANG X Q, et al. Data science for oceanography: From small data to big data[J]. Big Earth Data, 2022, 6(2): 236-250. 本文引用 [4]

[9]	王辉, 刘娜, 逄仁波, 等. 全球海洋预报与科学大数据[J]. 科学通报, 2015, 60(S1):479-484. WANG H, LIU N, PANG R B, et al. Global ocean forecasting and scientific big data[J]. Chinese Science Bulletin, 2015, 60(S1): 479-484. 本文引用 [2]

[10]	MARTIN S. An introduction to ocean remote sensing[M]. 2nd ed. Cambridge: Cambridge University Press, 2014. 本文引用 [2]

[11]	戴洪磊, 牟乃夏, 王春玉, 等. 我国海洋浮标发展现状及趋势[J]. 气象水文海洋仪器, 2014, 31(2):118-121,125. DAI H L, MOU N X, WANG C Y, et al. Development status and trend of ocean buoy in China[J]. Meteorological, Hydrological and Marine Instruments, 2014, 31(2): 118-121, 125. 本文引用 [1]

[12]	WULDER M A, COOPS N C. Satellites: Make earth obser-vations open access[J]. Nature, 2014, 513(7516): 30-31. 本文引用 [1]

[13]

张蕾, 张国航, 毛凌野. 中国海洋卫星二十年[EB/OL].(2022-05-16)[2023-09-02]. http://finance.people.com.cn/n1/2022/0516/c1004-32422462.html. http://finance.people.com.cn/n1/2022/0516/c1004-32422462.html ZHANG L, ZHANG G H, MAO L Y. Twenty years of China’s ocean satellites [EB/OL]. (2022-05-16)[2023-09-02]. http://finance.people.com.cn/n1/2022/0516/c1004-32422462.html. http://finance.people.com.cn/n1/2022/0516/c1004-32422462.html 本文引用 [2]

[14]	SONG T, PANG C, HOU B Y, et al. A review of artificial intelligence in marine science[J]. Frontiers in Earth Science, 2023, 11: 1090185. 本文引用 [2]

[15]	KRASNOPOLSKY V M. The application of neural networks in the earth system sciences[M]. [S.l.]: Springer, 2013. 本文引用 [1]

[16]	BOUKABARA S A, KRASNOPOLSKY V, STEWART J Q, et al. Leveraging modern artificial intelligence for remote sensing and NWP: Benefits and challenges[J]. Bulletin of the American Meteorological Society, 2019, 100(12): 473-491. 本文引用 [1]

[17]	BALL J E, ANDERSON D T, CHAN C S. Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community[J]. Journal of Applied Remote Sensing, 2017, 11(4): 1. 本文引用 [1]

[18]	WANG H Y, LI X F. Deepblue: Advanced convolutional neural network applications for ocean remote sensing[J]. IEEE Geoscience and Remote Sensing Magazine, 2024, 12(1): 138-161. 本文引用 [1]

[19]	宋振亚, 刘卫国, 刘鑫, 等. 海量数据驱动下的高分辨率海洋数值模式发展与展望[J]. 海洋科学进展, 2019, 37(2):161-170. SONG Z Y, LIU W G, LIU X, et al. Research progress and perspective of the key technologies for ocean numerical model driven by the mass data[J]. Advances in Marine Science, 2019, 37(2): 161-170. 本文引用 [1]

[20]	孙苗, 符昱, 吕憧憬, 等. 深度学习在海洋大数据挖掘中的应用[J]. 科技导报, 2018, 36(17):83-90. SUN M, FU Y, LÜ C J, et al. Deep learning application in marine big data mining[J]. Science & Technology Review, 2018, 36(17): 83-90. 本文引用 [1]

[21]	PING B, SU F Z, MENG Y S. An improved DINEOF algorithm for filling missing values in spatio-temporal sea surface temperature data[J]. PLoS One, 2016, 11(5):e0155928. 本文引用 [1]

[22]	LIU X M, WANG M H. Gap filling of missing data for VIIRS global ocean color products using the DINEOF method[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(8): 4464-4476. 本文引用 [1]

[23]	DUCOURNAU A, FABLET R. Deep learning for ocean remote sensing: An application of convolutional neural networks for super-resolution on satellite-derived SST data[C]//Proceedings of the 2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS), December 4, 2016, Cancun, Mexico. 本文引用 [1]

[24]	PARK J, KIM J H, KIM H C, et al. Reconstruction of ocean color data using machine learning techniques in polar regions: Focusing on off Cape Hallett, Ross Sea[J]. Remote Sensing, 2019, 11(11): 1366. 本文引用 [1]

[25]	LI M, ZHANG R, LIU K F. Evolving a Bayesian network model with information flow for time series interpolation of multiple ocean variables[J]. Acta Oceanologica Sinica, 2021, 40(7): 249-262. 本文引用 [1]

[26]	RADIN C, NIEVES V. Machine-learning based recons-tructions of past regional sea level variability from proxy data[J]. Geophysical Research Letters, 2021, 48(23):e2021GL095382. 本文引用 [1]

[27]	TIAN T, CHENG L J, WANG G J, et al. Reconstructing ocean subsurface salinity at high resolution using a machine learning approach[J]. Earth System Science Data, 2022, 14(11): 5037-5060. 本文引用 [3]

[28]	CHEN Y H, LIU L, CHEN X E, et al. Data driven three-dimensional temperature and salinity anomaly reconstruction of the northwest Pacific Ocean[J]. Frontiers in Marine Science, 2023, 10: 1121334. 本文引用 [1]

[29]	CHELTON D B, SCHLAX M G, SAMELSON R M, et al. Global observations of large oceanic eddies[J]. Geophysical Research Letters, 2007, 34(15): L15606. 本文引用 [1]

[30]	DONG C M, MCWILLIAMS J C, LIU Y, et al. Global heat and salt transports by eddy movement[J]. Nature Commu-nications, 2014, 5: 3294. 本文引用 [1]

[31]

MCGILLICUDDY D J Jr. Mechanisms of physical-biological-biogeochemical interaction at the oceanic mesoscale[J]. Annual Review of Marine Science, 2016, 8: 125-159. https://doi.org/10.1146/annurev-marine-010814-015606 https://www.ncbi.nlm.nih.gov/pubmed/26359818 本文引用 [1] 摘要 Mesoscale phenomena are ubiquitous and highly energetic features of ocean circulation. Their influence on biological and biogeochemical processes varies widely, stemming not only from advective transport but also from the generation of variations in the environment that affect biological and chemical rates. The ephemeral nature of mesoscale features in the ocean makes it difficult to elucidate the attendant mechanisms of physical-biological-biogeochemical interaction, necessitating the use of multidisciplinary approaches involving in situ observations, remote sensing, and modeling. All three aspects are woven through this review in an attempt to synthesize current understanding of the topic, with particular emphasis on novel developments in recent years.

[32]	BRANNIGAN L. Intense submesoscale upwelling in anti-cyclonic eddies[J]. Geophysical Research Letters, 2016, 43(7): 3360-3369. 本文引用 [1]

[33]	LGUENSAT R, SUN M, FABLET R, et al. EddyNet: A deep neural network for pixel-wise classification of oceanic eddies[C]//Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 22-27, 2018, Valencia, Spain. IEEE, 2018: 1764-1767. 本文引用 [1]

[34]	FRANZ K, ROSCHER R, MILIOTO A, et al. Ocean eddy identification and tracking using neural networks[C]//Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), July 22-27, 2018, Valencia, Spain. IEEE, 2018: 6887-6890. 本文引用 [1]

[35]	XU G J, CHENG C, YANG W X, et al. Oceanic eddy identification using an AI scheme[J]. Remote Sensing, 2019, 11(11): 1349. 本文引用 [1]

[36]	XU G J, XIE W H, DONG C M, et al. Application of three deep learning schemes into oceanic eddy detection[J]. Frontiers in Marine Science, 2021, 8: 672334. 本文引用 [1]

[37]	DUO Z J, WANG W K, WANG H Z. Oceanic mesoscale eddy detection method based on deep learning[J]. Remote Sensing, 2019, 11(16): 1921. 本文引用 [1]

[38]	CAO L J, ZHANG D J, ZHANG X F, et al. Detection and identification of mesoscale eddies in the South China Sea based on an artificial neural network model—YOLOF and remotely sensed data[J]. Remote Sensing, 2022, 14(21):5411. 本文引用 [1]

[39]	HANG R L, LI G, XUE M, et al. Identifying oceanic eddy with an edge-enhanced multiscale convolutional network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 9198-9207. 本文引用 [1]

[40]	LIU Y J, ZHENG Q A, LI X F. Characteristics of global ocean abnormal mesoscale eddies derived from the fusion of sea surface height and temperature data by deep learning[J]. Geophysical Research Letters, 2021, 48(17): e2021GL094772. 本文引用 [1]

[41]	ZHANG Y Y, LIU N, ZHANG Z Y, et al. Detection of Bering Sea slope mesoscale eddies derived from satellite altimetry data by an attention network[J]. Remote Sensing, 2022, 14(19): 4974. 本文引用 [1]

[42]	ZHAO N, HUANG B X, ZHANG X M, et al. Intelligent identification of oceanic eddies in remote sensing data via dual-pyramid UNet[J]. Atmospheric and Oceanic Science Letters, 2023, 16(4): 100335. 本文引用 [1]

[43]	ZHAO Y X, FAN Z L, LI H T, et al. Symmetric net: End-to-end mesoscale eddy detection with multi-modal data fusion[J]. Frontiers in Marine Science, 2023, 10: 1174818. 本文引用 [1]

[44]	KOCHKOV D, YUVAL J, LANGMORE I, et al. Neural general circulation models for weather and climate[Z/OL]. arXiv, 2023: 2311.07222. https://arxiv.org/abs/2311.07222v3. https://arxiv.org/abs/2311.07222v3 本文引用 [1]

[45]	WANG C, LI X F. Deep learning in extracting tropical cyclone intensity and wind radius information from satellite infrared images—A review[J]. Atmospheric and Oceanic Science Letters, 2023, 16(4): 100373. 本文引用 [1]

[46]	SUN L N, ZHANG J, MENG J M. A study of the spatial-temporal distribution and propagation characteristics of internal waves in the Andaman Sea using MODIS[J]. Acta Oceanologica Sinica, 2019, 38(7): 121-128. 本文引用 [1]

[47]	LI X F, JACKSON C R, PICHEL W G. Internal solitary wave refraction at Dongsha atoll, South China Sea[J]. Geophysical Research Letters, 2013, 40(12): 3128-3132. 本文引用 [1]

[48]	BAI X L, LI X F, LAMB K G, et al. Internal solitary wave reflection near Dongsha atoll, the South China Sea[J]. Journal of Geophysical Research: Oceans, 2017, 122(10): 7978-7991. 本文引用 [1]

[49]	ZHANG X D, LI X F, ZHANG T. Characteristics and generations of internal wave in the Sulu Sea inferred from optical satellite images[J]. Journal of Oceanology and Limnology, 2020, 38(5): 1435-1444. 本文引用 [1]

[50]	ZHENG Q A, YUAN Y L, KLEMAS V, et al. Theoretical expression for an ocean internal soliton synthetic aperture radar image and determination of the soliton characteristic half width[J]. Journal of Geophysical Research: Oceans, 2001, 106(C12): 31415-31423. 本文引用 [1]

[51]	PAN X Y, WANG J, ZHANG X D, et al. A deep-learning model for the amplitude inversion of internal waves based on optical remote-sensing images[J]. International Journal of Remote Sensing, 2018, 39(3): 607-618. 本文引用 [1]

[52]

LI X F, LIU B, ZHENG G, et al. Deep-learning-based information mining from ocean remote-sensing imagery[J]. National Science Review, 2020, 7(10): 1584-1605. https://doi.org/10.1093/nsr/nwaa047 https://www.ncbi.nlm.nih.gov/pubmed/34691490 本文引用 [1] 摘要 With the continuous development of space and sensor technologies during the last 40 years, ocean remote sensing has entered into the big-data era with typical five-V (volume, variety, value, velocity and veracity) characteristics. Ocean remote-sensing data archives reach several tens of petabytes and massive satellite data are acquired worldwide daily. To precisely, efficiently and intelligently mine the useful information submerged in such ocean remote-sensing data sets is a big challenge. Deep learning-a powerful technology recently emerging in the machine-learning field-has demonstrated its more significant superiority over traditional physical- or statistical-based algorithms for image-information extraction in many industrial-field applications and starts to draw interest in ocean remote-sensing applications. In this review paper, we first systematically reviewed two deep-learning frameworks that carry out ocean remote-sensing-image classifications and then presented eight typical applications in ocean internal-wave/eddy/oil-spill/coastal-inundation/sea-ice/green-algae/ship/coral-reef mapping from different types of ocean remote-sensing imagery to show how effective these deep-learning frameworks are. Researchers can also readily modify these existing frameworks for information mining of other kinds of remote-sensing imagery.© The Author(s) 2020. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.

[53]	WANG S F, TENG Y J, PERDIKARIS P. Understanding and mitigating gradient flow pathologies in physics-informed neural networks[J]. SIAM Journal on Scientific Computing, 2021, 43(5): 3055-3081. 本文引用 [1]

[54]	VASAVI S, DIVYA C, SARMA A S. Detection of solitary ocean internal waves from SAR images by using UNet and KDV solver technique[J]. Global Transitions Proceedings, 2021, 2(2): 145-151. 本文引用 [1]

[55]	ZHANG X D, WANG H Y, WANG S, et al. Oceanic internal wave amplitude retrieval from satellite images based on a data-driven transfer learning model[J]. Remote Sensing of Environment, 2022, 272: 112940. 本文引用 [1]

[56]	ZAKHVATKINA N, SMIRNOV V, BYCHKOVA I. Satellite SAR data-based sea ice classification: An overview[J]. Geosciences, 2019, 9(4): 152. 本文引用 [1]

[57]	XU Y, SCOTT K A. Sea ice and open water classification of SAR imagery using CNN-based transfer learning[C]//Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 23-28, 2017, Fort Worth, TX, USA. IEEE, 2017: 3262-3265. 本文引用 [1]

[58]	LI J X, WANG C, WANG S G, et al. Gaofen-3 sea ice detection based on deep learning[C]//Proceedings of the 2017 Progress in Electromagnetics Research Symposium-Fall (PIERS-FALL), November 19-22, 2017, Singapore. IEEE, 2017: 933-939. 本文引用 [1]

[59]	GAO Y H, GAO F, DONG J Y, et al. Transferred deep learning for sea ice change detection from synthetic-aperture radar images[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(10): 1655-1659. 本文引用 [1]

[60]	BOULZE H, KOROSOV A, BRAJARD J. Classification of sea ice types in Sentinel-1 SAR data using convolutional neural networks[J]. Remote Sensing, 2020, 12(13): 2165. 本文引用 [1]

[61]	REN Y B, LI X F, YANG X F, et al. Development of a dual-attention UNet model for sea ice and open water classification on SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4010205. 本文引用 [1]

[62]	GONÇALVES BENTO C, LYNCH HEATHER J. Fine-scale sea ice segmentation for high-resolution satellite imagery with weakly-supervised CNNs[J]. Remote Sensing, 2021, 13(18): 3562. 本文引用 [1]

[63]	ZHANG J D, ZHANG W Y, HU Y X, et al. An improved sea ice classification algorithm with Gaofen-3 dual-polarization SAR data based on deep convolutional neural networks[J]. Remote Sensing, 2022, 14(4): 906. 本文引用 [1]

[64]	LINS I D, ARAUJO M, MOURA M D C, et al. Prediction of sea surface temperature in the tropical Atlantic by support vector machines[J]. Computational Statistics & Data Analysis, 2013, 61: 187-198. 本文引用 [1]

[65]	PATIL K, DEO M C. Basin-scale prediction of sea surface temperature with artificial neural networks[J]. Journal of Atmospheric and Oceanic Technology, 2018, 35(7): 1441-1455. 本文引用 [1]

[66]	WEI L, GUAN L, QU L Q, et al. Prediction of sea surface temperature in the South China Sea by artificial neural networks[C]//Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 28-August 2, 2019, Yokohama, Japan. IEEE, 2019: 8158-8161. 本文引用 [1]

[67]	PETERSIK P J, DIJKSTRA H A. Probabilistic forecasting of El Niño using neural network models[J]. Geophysical Research Letters, 2020, 47(6): e2019GL086423. 本文引用 [1]

[68]	朱贵重, 胡松. 基于LSTM-RNN的海水表面温度模型研究[J]. 应用海洋学学报, 2019, 38(2):191-197. ZHU G C, HU S. Study on sea surface temperature model based on LSTM-RNN[J]. Journal of Applied Oceanography, 2019, 38(2): 191-197. 本文引用 [1]

[69]	ZHANG Z, PAN X L, JIANG T, et al. Monthly and quarterly sea surface temperature prediction based on gated recurrent unit neural network[J]. Journal of Marine Science and Engineering, 2020, 8(4): 249. 本文引用 [1]

[70]	XIAO C J, CHEN N C, HU C L, et al. A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data[J]. Environ-mental Modelling & Software, 2019, 120: 104502. 本文引用 [1]

[71]	MAHESH A, EVANS M, JAIN G, et al. Forecasting El Niño with convolutional and recurrent neural networks[C]//Neurips 2019 Workshop-Tackling Climate Change with Machine Learning, Vancouver, 2019. 本文引用 [1]

[72]	HAM Y G, KIM J H, LUO J J. Deep learning for multi-year ENSO forecasts[J]. Nature, 2019, 573(7775): 568-572. 本文引用 [3]

[73]	XIAO C J, CHEN N C, HU C L, et al. Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach[J]. Remote Sensing of Environment, 2019, 233: 111358. 本文引用 [1]

[74]	GUPTA M, KODAMANA H, SANDEEP S. Prediction of ENSO beyond spring predictability barrier using deep convolutional LSTM networks[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1501205. 本文引用 [1]

[75]	CACHAY S R, ERICKSON E, BUCKER A, et al. Graph neural networks for improved El Niño forecasting[Z/OL]. arXiv, 2021: 2012. 01598. https://arxiv.org/abs/2012.01598. https://arxiv.org/abs/2012.01598 本文引用 [1]

[76]	ZHOU L, ZHANG R H. A hybrid neural network model for ENSO prediction in combination with principal oscillation pattern analyses[J]. Advances in Atmospheric Sciences, 2022, 39(6): 889-902. 本文引用 [1]

[77]	LIANG X S, XU F, RONG Y N, et al. El Niño Modoki can be mostly predicted more than 10 years ahead of time[J]. Scientific Reports, 2021, 11(1): 17860. 本文引用 [1]

[78]	GLAHN B, TAYLOR A, KURKOWSKI N, et al. The role of the SLOSH model in National Weather Service storm surge forecasting[J]. National Weather Digest, 2009, 33: 3-14. 本文引用 [1]

[79]	LEE T L. Neural network prediction of a storm surge[J]. Ocean Engineering, 2006, 33(3/4): 483-494. 本文引用 [1]

[80]	LEE T L. Back-propagation neural network for the prediction of the short-term storm surge in Taichung harbor, Taiwan[J]. Engineering Applications of Artificial Intelligence, 2008, 21(1): 63-72. 本文引用 [1]

[81]	JIANG S Q, LIU Q. The BP neural network optimized by Beetle Antenna Search Algorithm for storm surge prediction[C]//The 30th International Ocean and Polar Engineering Conference, 2020: ISOPE-I-20-3286. 本文引用 [1]

[82]	CHAO W T, YOUNG C C, HSU T W, et al. Long-lead-time prediction of storm surge using artificial neural networks and effective typhoon parameters: Revisit and deeper insight[J]. Water, 2020, 12(9): 2394. 本文引用 [1]

[83]	KIM S Y, MATSUMI Y, SHIOZAKI S, et al. A study of a real-time storm surge forecast system using a neural network at the Sanin Coast, Japan[C]//Proceedings of the 2012 Oceans, October 14-19, 2012, Hampton Roads, VA, USA. IEEE, 2012: 1-7. 本文引用 [1]

[84]	KIM S W, MELBY J A, NADAL-CARABALLO N C, et al. A time-dependent surrogate model for storm surge prediction based on an artificial neural network using high-fidelity synthetic hurricane modeling[J]. Natural Hazards, 2015, 76(1): 565-585. 本文引用 [1]

[85]	KIM S, MATSUMI Y, PAN S Q, et al. A real-time forecast model using artificial neural network for after-runner storm surges on the Tottori coast, Japan[J]. Ocean Engineering, 2016, 122: 44-53. 本文引用 [1]

[86]	KIM S W, LEE A, MUN J. A surrogate modeling for storm surge prediction using an artificial neural network[J]. Journal of Coastal Research, 2018, 85: 866-870. 本文引用 [3]

[87]	KIM S, PAN S Q, MASE H. Artificial neural network-based storm surge forecast model: Practical application to Sakai Minato, Japan[J]. Applied Ocean Research, 2019, 91: 101871. 本文引用 [1]

[88]	RAJASEKARAN S, GAYATHRI S, LEE T L. Support vector regression methodology for storm surge predictions[J]. Ocean Engineering, 2008, 35(16): 1578-1587. 本文引用 [1]

[89]	YOU S H, SEO J W. Storm surge prediction using an artificial neural network model and cluster analysis[J]. Natural Hazards, 2009, 51(1): 97-114. 本文引用 [1]

[90]	RUMELHART D E, HINTON G E, WILLIAMS R J. Lear-ning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536. 本文引用 [1]

[91]

SCHMIDHUBER J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61: 85-117. https://www.ncbi.nlm.nih.gov/pubmed/25462637 本文引用 [1] 摘要 In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

[92]	雷森, 史振威, 石天阳, 等. 基于递归神经网络的风暴潮增水预测[J]. 智能系统学报, 2017, 12(5):640-644. LEI S, SHI Z W, SHI T Y, et al. Prediction of storm surge based on recurrent neural network[J]. CAAI Transactions on Intelligent Systems, 2017, 12(5): 640-644. 本文引用 [1]

[93]	刘媛媛, 张丽, 李磊, 等. 基于多变量LSTM神经网络模型的风暴潮临近预报[J]. 海洋通报, 2020, 39(6):689-694. LIU Y Y, ZHANG L, LI L, et al. Storm surge nowcasting based on multivariable LSTM neural network model[J]. Marine Science Bulletin, 2020, 39(6): 689-694. 本文引用 [1]

[94]	XIE W H, XU G J, ZHANG H C, et al. Developing a deep learning-based storm surge forecasting model[J]. Ocean Modelling, 2023, 182: 102179. 本文引用 [1]

[95]	谢文鸿, 徐广珺, 董昌明. 基于ConvLSTM机器学习的风暴潮漫滩预报研究[J]. 大气科学学报, 2022, 45(5):674-687. XIE W H, XU G J, DONG C M. Research on storm surge floodplain prediction based on ConvLSTM machine learning[J]. Transactions of Atmospheric Sciences, 2022, 45(5): 674-687. 本文引用 [1]

[96]	DEO M C, SRIDHAR NAIDU C. Real time wave forecasting using neural networks[J]. Ocean Engineering, 1998, 26(3): 191-203. 本文引用 [1]

[97]	LONDHE S N, PANCHANG V. One-day wave forecasts based on artificial neural networks[J]. Journal of Atmospheric and Oceanic Technology, 2006, 23(11): 1593-1603. 本文引用 [2]

[98]	KALOOP M R, KUMAR D, ZARZOURA F, et al. A wavelet-Particle swarm optimization-Extreme learning machine hybrid modeling for significant wave height prediction[J]. Ocean Engineering, 2020, 213: 107777. 本文引用 [1]

[99]	MANDAL S, PRABAHARAN N. Ocean wave forecasting using recurrent neural networks[J]. Ocean Engineering, 2006, 33(10): 1401-1410. 本文引用 [1]

[100]

GAO S, HUANG J, LI Y R, et al. A forecasting model for wave heights based on a long short-term memory neural network[J]. Acta Oceanologica Sinica, 2021, 40(1): 62-69. 本文引用 [1]

[101]

LU P, LIANG S, ZOU G, et al. M-LSTM, A hybrid pre-diction model for wave heights[J]. Journal of Nonlinear and Convex Analysis, 2019, 20(5): 775-786. 本文引用 [1]

[102]

ZHOU S Y, BETHEL B J, SUN W J, et al. Improving significant wave height forecasts using a joint empirical mode decomposition-long short-term memory network[J]. Journal of Marine Science and Engineering, 2021, 9(7): 744. 本文引用 [2]

[103]

FAN S T, XIAO N H, DONG S. A novel model to predict significant wave height based on long short-term memory network[J]. Ocean Engineering, 2020, 205: 107298. 本文引用 [1]

[104]

ZHOU S Y, XIE W H, LU Y X, et al. ConvLSTM-based wave forecasts in the south and east China Seas[J]. Frontiers in Marine Science, 2021, 8: 680079. 本文引用 [3]

[105]

BAI G, WANG Z F, ZHU X Y, et al. Development of a 2-D deep learning regional wave field forecast model based on convolutional neural network and the application in South China Sea[J]. Applied Ocean Research, 2022, 118: 103012. 本文引用 [1]

[106]

张峰, 王琪, 卢美, 等. 基于人工神经网络的海流预报研究[J]. 海洋预报, 2018, 35(4):41-46. ZHANG F, WANG Q, LU M, et al. Study of tidal current prediction based on artificial neural network[J]. Marine Forecasts, 2018, 35(4): 41-46. 本文引用 [1]

[107]

XIN L, HU S, WANG F, et al. Using a deep-learning approach to infer and forecast the Indonesian throughflow transport from sea surface height[J]. Frontiers in Marine Science, 2023, 10: 1079286. 本文引用 [1]

[108]

PARK S, BYUN J, SHIN K S, et al. Ocean current pre-diction based on machine learning for deciding handover priority in underwater wireless sensor networks[C]//Proceedings of the 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), February 19-21, 2020, Fukuoka, Japan. IEEE, 2020: 505-509. 本文引用 [1]

[109]

XIONG W, XIANG Y F, WU H, et al. AI-GOMS: Large AI-driven global ocean modeling system[Z/OL]. arXiv, 2023: 2308.03152. https://arxiv.org/abs/2308.03152v2. https://arxiv.org/abs/2308.03152v2 本文引用 [1]

[110]

MELLOR G L, YAMADA T. Development of a turbulence closure model for geophysical fluid problems[J]. Reviews of Geophysics, 1982, 20(4): 851-875. 本文引用 [1]

[111]

PACANOWSKI R C, PHILANDER S G H. Parameterization of vertical mixing in numerical models of tropical oceans[J]. Journal of Physical Oceanography, 1981, 11(11): 1443-1451. 本文引用 [1]

[112]

LARGE W G, MCWILLIAMS J C, DONEY S C. Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization[J]. Reviews of Geophy-sics, 1994, 32(4): 363-403. 本文引用 [1]

[113]

CHEN D K, ROTHSTEIN L M, BUSALACCHI A J. A hybrid vertical mixing scheme and its application to tropical ocean models[J]. Journal of Physical Oceanography, 1994, 24(10): 2156-2179. 本文引用 [1]

[114]

FOX-KEMPER B, FERRARI R, HALLBERG R. Para-meterization of mixed layer eddies. Part I: Theory and diagnosis[J]. Journal of Physical Oceanography, 2008, 38(6): 1145-1165. 本文引用 [1]

[115]

SMAGORINSKY J. General circulation experiments with the primitive equations[J]. Monthly Weather Review, 1963, 91(3): 99-164. 本文引用 [2]

[116]

GENT P R, MCWILLIAMS J C. Isopycnal mixing in ocean circulation models[J]. Journal of Physical Oceanography, 1990, 20(1): 150-155. 本文引用 [1]

[117]

FOX-KEMPER B, BACHMAN S, PEARSON B, et al. Principles and advances in subgrid modeling for eddy-rich simulations[J]. CLIVAR Exchanges, 2014, 19(65): 42-46. 本文引用 [1]

[118]

DANABASOGLU G, MCWILLIAMS J C, GENT P R. The role of mesoscale tracer transports in the global ocean circulation[J]. Science, 1994, 264(5162): 1123-1126. https://www.ncbi.nlm.nih.gov/pubmed/17744896 本文引用 [1] 摘要 Ocean models routinely used in simulations of the Earth's climate do not resolve mesoscale eddies because of the immense computational cost. A new parameterization of the effects of these eddies has been implemented in a widely used model. A comparison of its solution with that of the conventional parameterization shows significant improvements in the global temperature distribution, the poleward and surface heat fluxes, and the locations of deep-water formation.

[119]

BERLOFF P S. Random-forcing model of the mesoscale oceanic eddies[J]. Journal of Fluid Mechanics, 2005, 529: 71-95. 本文引用 [1]

[120]

PORTA MANA P, ZANNA L. Toward a stochastic parame-terization of ocean mesoscale eddies[J]. Ocean Modelling, 2014, 79: 1-20. 本文引用 [1]

[121]

ZANNA L, PORTA MANA P, ANSTEY J, et al. Scale-aware deterministic and stochastic parametrizations of eddy-mean flow interaction[J]. Ocean Modelling, 2017, 111: 66-80. 本文引用 [1]

[122]

DURAISAMY K, IACCARINO G, XIAO H. Turbulence modeling in the age of data[J]. Annual Review of Fluid Mechanics, 2019, 51: 357-377. 本文引用 [1]

[123]

张涛, 谢丰, 薛巍, 等. 格点大气环流模式GAMIL2参数不确定性的量化分析与优化[J]. 地球物理学报, 2016, 59(2):465-475. https://doi.org/10.6038/cjg20160206 摘要 物理过程参数化方案的不确定性是目前气候系统模式不确定性的重要来源之一.随着模式内在复杂度攀升,模拟场景多样化,参数化方案中基于先验的和人工的物理参数选取方法已经逐步成为限制模式模拟能力的瓶颈之一.为此,本文设计并提出了初选与寻优相结合的两步法参数优化方案.初选阶段用全因子采样方法对不确定参数空间进行初始敏感性分析,估计最优解所在区域;寻优步采用单纯型下山法,基于初选阶段确定的参数组合快速寻优.将两步法应用于中国科学院大气物理研究所(英文缩写:IAP)大气科学和地球流体力学数值模拟国家重点实验室(英文缩写:LASG)格点大气模式第2版:GAMIL2,选取其深对流方案和云量方案中的3个重要参数开展寻优,优化以综合减小模式降水、风场、温度、湿度、位势高度以及辐射通量的误差为目标.这些变量用GAMIL2标准版本标准化后形成单一的目标. 结果显示,优化后的目标函数值比GAMIL2 标准版本改进了7.5%.机理分析表明,调优后的参数优化了大气中的水汽凝结作用,进而减少模式的湿度偏差,改进云量的模拟效果;同时水汽凝结作用的变化通过大气内部动力和热力相互作用及响应影响温度、位势高度和风场的模拟. ZHANG T, XIE F, XUE W, et al. Quantification and optimization of parameter uncertainty in the grid-point atmospheric model GAMIL2[J]. Chinese Journal of Geo-physics, 2016, 59(2): 465-475. 本文引用 [2]

[124]

JIANG G Q, XU J, WEI J. A deep learning algorithm of neural network for the parameterization of typhoon-ocean feedback in typhoon forecast models[J]. Geophysical Research Letters, 2018, 45(8): 3706-3716. 本文引用 [2]

[125]

BOLTON T, ZANNA L. Applications of deep learning to ocean data inference and subgrid parameterization[J]. Journal of Advances in Modeling Earth Systems, 2019, 11(1): 376-399. 本文引用 [2]

[126]

GREATBATCH R J, ZHAI X M, KOHLMANN J D, et al. Ocean eddy momentum fluxes at the latitudes of the Gulf Stream and the Kuroshio extensions as revealed by satellite data[J]. Ocean Dynamics, 2010, 60(3): 617-628. 本文引用 [1]

[127]

GREATBATCH R J, ZHAI X, CLAUS M, et al. Transport driven by eddy momentum fluxes in the Gulf Stream extension region[J]. Geophysical Research Letters, 2010, 37: L24401. 本文引用 [1]

[128]

KANG D J, CURCHITSER E N. Energetics of eddy-mean flow interactions in the Gulf Stream region[J]. Journal of Physical Oceanography, 2015, 45(4): 1103-1120. 本文引用 [1]

[129]

WATERMAN S, JAYNE S. Eddy-mean flow interactions in the along-stream development of a western boundary current jet: An idealized model study[J]. Journal of Physical Oceanography, 2011, 41(4): 682-707. 本文引用 [1]

[130]

WATERMAN S, HOGG N, JAYNE S. Eddy-mean flow interaction in the Kuroshio extension region[J]. Journal of Physical Oceanography, 2011, 41(6): 1182-1194, 1197. 本文引用 [1]

[131]

GOODFELLOW I, BENGIO Y, COURVILLE A. Deep Learning[M]. [S.l.]: MIT Press, 2016. 本文引用 [1]

[132]

TRACEY B D, DURAISAMY K, ALONSO J J. A machine learning strategy to assist turbulence model development[C]//Proceedings of the 53rd AIAA Aerospace Sciences Meeting. Kissimmee, Florida. Reston, Virginia: AIAA, 2015: AIAA 2015-1287. 本文引用 [1]

[133]

LING J L, KURZAWSKI A, TEMPLETON J. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance[J]. Journal of Fluid Mechanics, 2016, 807: 155-166. 本文引用 [2]

[134]

LING J L, JONES R, TEMPLETON J. Machine learning strategies for systems with invariance properties[J]. Journal of Computational Physics, 2016, 318: 22-35. 本文引用 [2]

[135]

MAULIK R, SAN O. A neural network approach for the blind deconvolution of turbulent flows[J]. Journal of Fluid Mechanics, 2017, 831: 151-181. 本文引用 [1]

[136]

SAN O, MAULIK R. Extreme learning machine for reduced order modeling of turbulent geophysical flows[J]. Physical Review E, 2018, 97: 042322. 本文引用 [1]

[137]

MAULIK R, SAN O, RASHEED A, et al. Subgrid modelling for two-dimensional turbulence using neural networks[J]. Journal of Fluid Mechanics, 2019, 858: 122-144. https://doi.org/10.1017/jfm.2018.770 本文引用 [2] 摘要 In this investigation, a data-driven turbulence closure framework is introduced and deployed for the subgrid modelling of Kraichnan turbulence. The novelty of the proposed method lies in the fact that snapshots from high-fidelity numerical data are used to inform artificial neural networks for predicting the turbulence source term through localized grid-resolved information. In particular, our proposed methodology successfully establishes a map between inputs given by stencils of the vorticity and the streamfunction along with information from two well-known eddy-viscosity kernels. Through this we predict the subgrid vorticity forcing in a temporally and spatially dynamic fashion. Our study is both a priori and a posteriori in nature. In the former, we present an extensive hyper-parameter optimization analysis in addition to learning quantification through probability-density-function-based validation of subgrid predictions. In the latter, we analyse the performance of our framework for flow evolution in a classical decaying two-dimensional turbulence test case in the presence of errors related to temporal and spatial discretization. Statistical assessments in the form of angle-averaged kinetic energy spectra demonstrate the promise of the proposed methodology for subgrid quantity inference. In addition, it is also observed that some measure of a posteriori error must be considered during optimal model selection for greater accuracy. The results in this article thus represent a promising development in the formalization of a framework for generation of heuristic-free turbulence closures from data.

[138]

CRUZ M A, THOMPSON R L, SAMPAIO L E B, et al. The use of the Reynolds force vector in a physics informed machine learning approach for predictive turbulence modeling[J]. Computers & Fluids, 2019, 192: 104258. 本文引用 [1]

[139]

ZHOU Z D, HE G W, WANG S Z, et al. Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network[J]. Computers & Fluids, 2019, 195: 104319. 本文引用 [1]

[140]

LEITH C E. Diffusion approximation for two-dimensional turbulence[J]. The Physics of Fluids, 1968, 11(3): 671-672. 本文引用 [2]

[141]

ZANNA L, BOLTON T. Data-driven equation discovery of ocean mesoscale closures[J]. Geophysical Research Letters, 2020, 47(17): e2020GL088376. 本文引用 [3]

[142]

SALEHIPOUR H, PELTIER W R. Deep learning of mixing by two ‘atoms’ of stratified turbulence[J]. Journal of Fluid Mechanics, 2019, 861: R4. 本文引用 [1]

[143]

ZHU Y C, ZHANG R H, MOUM J N, et al. Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations[J]. National Science Review, 2022, 9(8): nwac044. 本文引用 [1]

[144]

HAN G Q, CEN H B, JIANG J H, et al. Applying machine learning in devising a parsimonious ocean mixing parameterization scheme[J]. Deep Sea Research Part II: Topical Studies in Oceanography, 2022, 203: 105163. 本文引用 [3]

[145]

LALOYAUX P, BONAVITA M, CHRUST M, et al. Exploring the potential and limitations of weak-constraint 4D-Var[J]. Quarterly Journal of the Royal Meteorological Society, 2020, 146(733): 4067-4082. 本文引用 [1]

[146]

SCHUHEN N, THORARINSDOTTIR T L, GNEITING T. Ensemble model output statistics for wind vectors[J]. Monthly Weather Review, 2012, 140(10): 3204-3219. 本文引用 [1]

[147]

BARAN Á, LERCH S, EL AYARI M, et al. Machine learning for total cloud cover prediction[J]. Neural Com-puting and Applications, 2021, 33(7): 2605-2620. 本文引用 [1]

[148]

GRÖNQUIST P, YAO C Y, BEN-NUN T, et al. Deep learning for post-processing ensemble weather forecasts[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2021, 379(2194): 20200092. 本文引用 [1]

[149]

RASP S, LERCH S. Neural networks for postprocessing ensemble weather forecasts[J]. Monthly Weather Review, 2018, 146(11): 3885-3900. 本文引用 [1]

[150]

LEINONEN J, NERINI D, BERNE A. Stochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network[J]. IEEE Transac-tions on Geoscience and Remote Sensing, 2021, 59(9): 7211-7223. 本文引用 [1]

[151]

MAKARYNSKYY O. Improving wave predictions with artificial neural networks[J]. Ocean Engineering, 2004, 31(5/6): 709-724. 本文引用 [1]

[152]

DESHMUKH A N, DEO M C, BHASKARAN P K, et al. Neural-network-based data assimilation to improve nume-rical ocean wave forecast[J]. IEEE Journal of Oceanic Engineering, 2016, 41(4): 944-953. 本文引用 [1]

[153]

SUN D Y, HUANG W Y, LUO Y, et al. A deep learning-based bias correction method for predicting ocean surface waves in the northwest Pacific Ocean[J]. Geophysical Research Letters, 2022, 49(23): e2022GL100916. 本文引用 [1]

[154]

GRACIA S, OLIVITO J, RESANO J, et al. Improving accuracy on wave height estimation through machine learning techniques[J]. Ocean Engineering, 2021, 236: 108699. 本文引用 [1]

[155]

吕忻, 丁骏. 基于深度学习的潮位预报订正技术研究[J]. 海洋预报, 2022, 39(2):70-79. LÜ X, DING J. Study on the correction technology of tide level forecast based on deep learning[J]. Marine Forecasts, 2022, 39(2): 70-79. 本文引用 [1]

[156]

TEDESCO P, RABAULT J, SAETRA M L, et al. Bias correction of operational storm surge forecasts using neural networks[Z/OL]. arXiv, 2023: 2301.00862. https://arxiv.org/abs/2301.00892v1. https://arxiv.org/abs/2301.00892v1 本文引用 [1]

[157]

MAO K, GAO F, ZHANG S Q, et al. An initial field intelligent correcting algorithm for numerical forecasting based on artificial neural networks under the conditions of limited observations: Part I—Focusing on ocean tempera-ture[J]. Journal of Marine Science and Engineering, 2022, 10(3): 311. 本文引用 [1]

[158]

WOLFF T D, CARRILLO H, LUIS M, et al. Assessing physics informed neural networks in ocean modelling and climate change applications[C]// Modeling Oceans and Climate Change Workshop at ICLR 2021, May 2021, Santiago (Virtual), Chile, 2021. HAL Id:hal-03262684. 本文引用 [1]

[159]

RUDY S H, BRUNTON S L, PROCTOR J L, et al. Data-driven discovery of partial differential equations[J]. Science Advances, 2017, 3(4): e1602614. 本文引用 [1]

[160]

RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics informed deep learning (Part I): Data-driven solutions of nonlinear partial differential equations[Z/OL]. arXiv, 2017: 1711.10561. https://arxiv.org/abs/1711.10561v1. https://arxiv.org/abs/1711.10561v1 本文引用 [1]

[161]

RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 2019, 378: 686-707. 本文引用 [5]

[162]

BLECHSCHMIDT J, ERNST O G. Three ways to solve partial differential equations with neural networks—A review[J]. GAMM-Mitteilungen, 2021, 44(2): e202100006. 本文引用 [1]

[163]

SCHNEIDER T, LAN S W, STUART A, et al. Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high-resolution simulations[J]. Geophysical Research Letters, 2017, 44(24): 12396-12417. 本文引用 [1]

[164]

ZANNA L, BRANKART J M, HUBER M, et al. Uncer-tainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions[J]. Quarterly Journal of the Royal Meteorological Society, 2019, 145(S1): 160-175. 本文引用 [1]

[165]

BEUCLER T, PRITCHARD M, RASP S, et al. Enforcing analytic constraints in neural networks emulating physical systems[J]. Physical Review Letters, 2021, 126(9):098302. 本文引用 [1]

[166]

GUILLOT J, KOENIG G, MINBASHIAN K, et al. Partial differential equations for oceanic artificial intelligence[J]. ESAIM: Proceedings and Surveys, 2021, 70: 137-146. 本文引用 [1]

[167]

KARNIADAKIS G E, KEVREKIDIS I G, LU L, et al. Physics-informed machine learning[J]. Nature Reviews Physics, 2021, 3(6): 422-440. 本文引用 [1]

[168]

LU L, JIN P, KARNIADAKIS G. DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators[Z/OL]. arXiv, 2019: 1910. 03193. https://arxiv.org/abs/1910.03193v1. https://arxiv.org/abs/1910.03193v1 本文引用 [1]

[169]

BHATTACHARYA K, HOSSEINI B, KOVACHKI N B, et al. Model reduction and neural networks for parametric PDEs[J]. The SMAI Journal of Computational Mathematics, 2021, 7: 121-157. 本文引用 [1]

[170]

LI Z Y, KOVACHKI N, AZIZZADENESHELI K, et al. Fourier neural operator for parametric partial differential equations[Z/OL]. arXiv, 2020: 2010. 08895. https://arxiv.org/abs/2010.08895v1. https://arxiv.org/abs/2010.08895v1 本文引用 [4]

[171]

GRIEVES M, VICKERS J. Digital twin: Mitigating unpre-dictable, undesirable emergent behavior in complex systems[M]//KAHLEN F J, FLUMERFELT S, ALVES A. Transdisciplinary perspectives on complex systems: New findings and approaches. [S.l.]: Springer, 2017: 85-113. 本文引用 [1]

[172]

IBRAHIM M, RASSÕLKIN A, VAIMANN T, et al. Overview on digital twin for autonomous electrical vehicles propulsion drive system[J]. Sustainability, 2022, 14(2):601. 本文引用 [1]

[173]

BAUER P, STEVENS B, HAZELEGER W. A digital twin of earth for the green transition[J]. Nature Climate Change, 2021, 11(2): 80-83. 本文引用 [1]

[174]

VOOSEN P. Europe builds ‘digital twin’ of Earth to hone climate forecasts[J]. Science, 2020, 370(6512): 16-17. 本文引用 [1]

[175]

JIANG P, MEINERT N, JORDÃO H, et al. Digital twin earth-coasts: Developing a fast and physics-informed surro-gate model for coastal floods via neural operators[Z/OL]. arXiv, 2021: 2110. 07100. https://arxiv.org/abs/2110.07100v1. https://arxiv.org/abs/2110.07100v1 本文引用 [1]

[176]

PATHAK J, SUBRAMANIAN S, HARRINGTON P, et al. FourCastNet: A global data-driven high-resolution weather model using adaptive Fourier neural operators[Z/OL]. arXiv, 2022: 2202. 11214. https://arxiv.org/abs/2202.11214v1. https://arxiv.org/abs/2202.11214v1 本文引用 [3]

[177]

SONG Z, DUAN Y, JIN W, et al. Omniverse-OpenDS: Enabling agile developments for complex driving scenarios via reconfigurable abstractions[M]// KRÖMKER H. HCI in mobility, transport, and automotive systems. [S.l.]: Springer, 2022: 72-87. https://doi.org/10.1007/978-3-031-04987-3_5. https://doi.org/10.1007/978-3-031-04987-3_5 本文引用 [1]

[178]

刘昌军, 吕娟, 任明磊, 等. 数字孪生淮河流域智慧防洪体系研究与实践[J]. 中国防汛抗旱, 2022, 32(1):47-53. LIU C J, LÜ J, REN M L, et al. Research and application of digital twin intelligent flood prevention system in Huaihe River Basin[J]. China Flood & Drought Management, 2022, 32(1): 47-53. 本文引用 [1]

[179]

甘郝新, 吴皓楠. 数字孪生珠江流域建设初探[J]. 中国防汛抗旱, 2022, 32(2):36-39. GAN H X, WU H N. Study on the construction of the digital twin Pearl River Basin[J]. China Flood & Drought Management, 2022, 32(2): 36-39. 本文引用 [1]

[180]

刘红彬, 申志强, 王轶泽, 等. 数字孪生模型在轴承套圈磨削加工中的应用[J]. 系统仿真学报, 2023, 35(3):557-567. https://doi.org/10.16182/j.issn1004731x.joss.21-1050 摘要 数字孪生模型能够有效地促进实际产品与产品模型间的虚实交互。针对调心滚子轴承套圈磨削过程中的磨削力，通过对磨削工作区各构件进行动力学与接触算法建模以及刚柔耦合处理，构建了轴承套圈沟道磨削加工数字孪生模型，完成了轴承套圈磨削工作区域在数字空间中的虚拟映射。并使用该模型对砂轮线速度、工件转速等工艺参数进行了分析与试验，证明了所构建数字孪生模型具有一定的有效性。该模型具有高保真性，可用于理解、预测和优化真实系统。 LIU H B, SHEN Z Q, WANG Y Z, et al. Application of digital twin model in grinding of bearing rings[J]. Journal of System Simulation, 2023, 35(3): 557-567. https://doi.org/10.16182/j.issn1004731x.joss.21-1050 本文引用 [1] 摘要 The digital twin model can effectively promote the virtual-real interaction between the actual product and the product model. Aiming at the grinding force generated in the grinding process of spherical roller bearings, this paper constructs the bearing ring raceway grinding process by performing dynamics, contact algorithm modeling and rigid-flexible coupling treatment on the components of the grinding work area. The digital twin model completes the virtual mapping of the grinding work area of the bearing ring in the digital space. The model is used to analyze and test the process parameters such as the grinding wheel linear speed and the workpiece speed, which proves the validity of the digital twin model constructed. The model has high fidelity and can be used to understand, predict and optimize real systems.

[181]

朱庆, 张利国, 丁雨淋, 等. 从实景三维建模到数字孪生建模[J]. 测绘学报, 2022, 51(6):1040-1049. https://doi.org/10.11947/j.AGCS.2022.20210640 摘要 从实景三维建模到数字孪生建模是国家数字经济和智慧社会建设与发展的基本需求。本文探讨了实景三维建模和数字孪生建模的关键技术内涵,介绍了数字乡村、未来社区和智能铁路等典型应用场景。广域范围实景三维建模在低成本高效数据采集和智能化自动化三维精细建模与动态更新方面面临挑战,城市级或重大工程级的数字孪生建模在全要素整体性的表征数据与机理模型集成表达方面还存在关键技术瓶颈。测绘技术亟须多学科交叉融合创新,突破天空地有机协同实时动态获取多细节层级实景三维数据、智能化处理多专业多尺度多模态时空数据、不完备数据条件下复杂场景的三维实体化精细建模、表征数据与机理模型结合的全生命周期数字孪生模型动态构建等核心关键技术,形成通用地理空间智能,实现测绘技术的高质量发展和对经济社会发展不可替代的更有力的基础支撑。 ZHU Q, ZHANG L G, DING Y L, et al. From real 3D modeling to digital twin modeling[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(6): 1040-1049. https://doi.org/10.11947/j.AGCS.2022.20210640 本文引用 [1] 摘要 From real 3D modeling to digital twin modeling is the basic requirement for the construction and development of the country's digital economy and smart society. This paper discusses the connotations and key technologies of real 3D modeling and digital twin modeling, and introduces three typical application scenarios: digital villages, future communities and intelligent railways. Wide-area real 3D modeling is facing challenges in low-cost and efficient data collection and intelligent automated modeling and dynamic updating. There are still key technical bottlenecks in the integrated expression of all-element holistic representation data and mechanism models for digital twin modeling at the city level or major engineering level. Surveying and mapping technology is in urgent need of multi-disciplinary cross-integration innovation, breakthroughs in organic collaboration between the sky and the ground, real-time and dynamic acquisition of multi-detailed real 3D data, automated intelligent processing of multi-specialty, multi-scale and multi-modal spatio-temporal data, and detailed 3D construction of complex scenes under incomplete data conditions. Core key technologies such as dynamic construction of full-life-cycle digital twin models that combine characterization data and mechanism models to form general geospatial intelligence to achieve high-quality development of surveying and mapping technology and an irreplaceable and more powerful basic support for economic and social development.

[182]

李猛, 聂铭, 和敬涵, 等. 基于数字孪生的柔性直流电网纵联保护原理[J]. 中国电机工程学报, 2022, 42(5):1773-1783. LI M, NIE M, HE J H, et al. Pilot protection of flexible DC grid based on digital twin[J]. Proceedings of the CSEE, 2022, 42(5): 1773-1783. 本文引用 [1]

[183]

黄艳, 喻杉, 罗斌, 等. 面向流域水工程防灾联合智能调度的数字孪生长江探索[J]. 水利学报, 2022, 53(3):253-269. HUANG Y, YU S, LUO B, et al. Development of the digital twin Changjiang River with the pilot system of joint and intelligent regulation of water projects for flood mana-gement[J]. Journal of Hydraulic Engineering, 2022, 53(3): 253-269. 本文引用 [1]

[184]

杨传书. 数字孪生技术在钻井领域的应用探索[J]. 石油钻探技术, 2022, 50(3):10-16. YANG C S. Exploration for the application of digital twin technology in drilling engineering[J]. Petroleum Drilling Techniques, 2022, 50(3): 10-16. 本文引用 [1]

[185]

王胜任, 郭岩, 乔兴华, 等. 基于数字孪生的飞机装配工艺技术研究[J]. 组合机床与自动化加工技术, 2021(8):131-134. https://doi.org/10.13462/j.cnki.mmtamt.2021.08.031 摘要 针对现有飞机产品在进行装配的过程中，传统工艺规划方式无法有效利用产品全生命周期的数据进行工艺方案优化，同时不能响应生产实际情况等问题，提出了利用数字孪生技术对动态数据进行有效的融合和管理，通过利用实时产生的数据来进行工艺决策，实时修正工艺方案，实现赛博和物理空间的虚实映射，在飞机装配工艺应用方面提供了实时决策和分析优化的方法，为计算机辅助工艺设计技术的智能化转变和飞机装配工艺瓶颈问题的解决提供了有效的途径。 WANG S R, GUO Y, QIAO X H, et al. Research on assembly process technology based on digital twin[J]. Modular Machine Tool & Automatic Manufacturing Tech-nique, 2021(8): 131-134. 本文引用 [1]

[186]

赵笑寒, 阳连丰, 罗超. 数字孪生模型在海上平台的应用[J]. 化学工程与装备, 2022(5):103-104. ZHAO X H, YANG L F, LUO C. Application of digital twin model in offshore platform[J]. Chemical Engineering & Equipment, 2022(5): 103-104. 本文引用 [1]

[187]

蒋冰, 姜晓轶, 吕憧憬, 等. 中国“数字海洋”工程进展研究[J]. 科技导报, 2018, 36(14):75-79. JIANG B, JIANG X Y, LÜ C J, et al. The research on digital ocean engineering of China[J]. Science & Technology Review, 2018, 36(14): 75-79. 本文引用 [1]

[188]

BARBIE A, PECH N, HASSELBRING W, et al. Develo-ping an underwater network of ocean observation systems with digital twin prototypes—A field report from the Baltic Sea[J]. IEEE Internet Computing, 2022, 26(3): 33-42. 本文引用 [1]

[189]

HESTNESS J, NARANG S, ARDALANI N, et al. Deep learning scaling is predictable, empirically[Z/OL]. arXiv, 2017: 1712.00409. https://arxiv.org/abs/1712.00409. https://arxiv.org/abs/1712.00409 本文引用 [1]

[190]

ROSENFELD J S, ROSENFELD A, BELINKOV Y, et al. A constructive prediction of the generalization error across scales[Z/OL]. arXiv, 2019: 1909. 12673. https://arxiv.org/abs/1909.12673v1. https://arxiv.org/abs/1909.12673v1 本文引用 [1]

[191]

KAPLAN J, MCCANDLISH S, HENIGHAN T, et al. Scaling laws for neural language models[Z/OL]. arXiv, 2020: 2001. 08361. https://arxiv.org/abs/2001.08361v1. https://arxiv.org/abs/2001.08361v1 本文引用 [1]

[192]

JONES A L. Scaling scaling laws with board games[Z/OL]. arXiv, 2021: 2104. 03113. https://arxiv.org/abs/2104.03113v1. https://arxiv.org/abs/2104.03113v1 本文引用 [1]

[193]

SEVILLA J, HEIM L, HO A, et al. Compute trends across three eras of machine learning[C]//Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN), July 18-23, 2022, Padua, Italy. IEEE, 2022: 1-8. 本文引用 [3]

[194]

YUAN S, ZHAO H, ZHAO S, et al. A roadmap for big model[Z/OL]. arXiv, 2022: 2203. 14101. https://arxiv.org/abs/2203.14101v3. https://arxiv.org/abs/2203.14101v3 本文引用 [1]

[195]

BROWN T, MANN B, RYDER N, et al. Language models are few-shot learners[Z/OL]. arXiv, 2020: 2005. 14165. https://arxiv.org/abs/2005.14165v4. https://arxiv.org/abs/2005.14165v4 本文引用 [1]

[196]

黄哲. 应用AI大模型[N]. 中国计算机报, 2023-07-17 (10). HUANG Z. Application of AI large model[N]. China Computer News, 2023-07-17 (10). 本文引用 [1]

[197]

BI K F, XIE L X, ZHANG H H, et al. Accurate medium-range global weather forecasting with 3D neural networks[J]. Nature, 2023, 619(7970): 533-538. 本文引用 [1]

[198]

CHEN K, HAN T, GONG J C, et al. FengWu: Pushing the skillful global medium-range weather forecast beyond 10 days lead[Z/OL]. arXiv, 2023: 2304.02948. https://arxiv.org/abs/2304.02948. https://arxiv.org/abs/2304.02948 本文引用 [1]

[199]

LAM R, SANCHEZ-GONZALEZ A, WILLSON M, et al. GraphCast: Learning skillful medium-range global weather forecasting[Z/OL]. arXiv, 2023: 2212. 12794. https://arxiv.org/abs/2212.12794v2. https://arxiv.org/abs/2212.12794v2 本文引用 [1]

[200]

NGUYEN T, BRANDSTETTER J, KAPOOR A, et al. ClimaX: A foundation model for weather and climate[Z/OL]. arXiv, 2023: 2301.10343. https://arxiv.org/abs/2301.10343v5. https://arxiv.org/abs/2301.10343v5 本文引用 [1]

[201]

ZHANG Y C, LONG M S, CHEN K Y, et al. Skilful nowcasting of extreme precipitation with NowcastNet[J]. Nature, 2023, 619(7970): 526-532. 本文引用 [2]

[202]

范昕茹. 气象大模型, 让极端天气不再无解[N]. IT时报,2023-07-28(3). FAN X R. Big weather model, let extreme weather no longer have no solution[N]. IT Times, 2023-07-28(3). 本文引用 [1]