Journal articles on the topic 'Hybrid physics-data driven models'
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Zhang, C., H. Xue, G. Dong, H. Jing, and S. He. "RUNOFF ESTIMATION BASED ON HYBRID-PHYSICS-DATA MODEL." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 347–52. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-347-2022.
Groves, Declan, and Andy Way. "Hybrid data-driven models of machine translation." Machine Translation 19, no. 3-4 (November 2, 2006): 301–23. http://dx.doi.org/10.1007/s10590-006-9015-5.
Jørgensen, Ulrik, Pauline Røstum Belingmo, Brian Murray, Svein Peder Berge, and Armin Pobitzer. "Ship route optimization using hybrid physics-guided machine learning." Journal of Physics: Conference Series 2311, no. 1 (July 1, 2022): 012037. http://dx.doi.org/10.1088/1742-6596/2311/1/012037.
Sun, Jian, Kristopher A. Innanen, and Chao Huang. "Physics-guided deep learning for seismic inversion with hybrid training and uncertainty analysis." GEOPHYSICS 86, no. 3 (March 19, 2021): R303—R317. http://dx.doi.org/10.1190/geo2020-0312.1.
Yun, Seong-Jin, Jin-Woo Kwon, and Won-Tae Kim. "A Novel Digital Twin Architecture with Similarity-Based Hybrid Modeling for Supporting Dependable Disaster Management Systems." Sensors 22, no. 13 (June 24, 2022): 4774. http://dx.doi.org/10.3390/s22134774.
Wang, Jinjiang, Yilin Li, Robert X. Gao, and Fengli Zhang. "Hybrid physics-based and data-driven models for smart manufacturing: Modelling, simulation, and explainability." Journal of Manufacturing Systems 63 (April 2022): 381–91. http://dx.doi.org/10.1016/j.jmsy.2022.04.004.
Belov, Sergei, Sergei Nikolaev, and Ighor Uzhinsky. "Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics." International Journal of Turbomachinery, Propulsion and Power 5, no. 4 (November 9, 2020): 29. http://dx.doi.org/10.3390/ijtpp5040029.
Fernandes, Pedro Henrique Evangelista, Giovanni Corsetti Silva, Diogo Berta Pitz, Matteo Schnelle, Katharina Koschek, Christof Nagel, and Vinicius Carrillo Beber. "Data-Driven, Physics-Based, or Both: Fatigue Prediction of Structural Adhesive Joints by Artificial Intelligence." Applied Mechanics 4, no. 1 (March 8, 2023): 334–55. http://dx.doi.org/10.3390/applmech4010019.
Al Rashdan, Ahmad Y., Hany S. Abdel-Khalik, Kellen M. Giraud, Daniel G. Cole, Jacob A. Farber, William W. Clark, Abenezer Alemu, Marcus C. Allen, Ryan M. Spangler, and Athi Varuttamaseni. "A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection." Energies 15, no. 15 (August 3, 2022): 5640. http://dx.doi.org/10.3390/en15155640.
Cain, Sahar, Ali Risheh, and Negin Forouzesh. "A Physics-Guided Neural Network for Predicting Protein–Ligand Binding Free Energy: From Host–Guest Systems to the PDBbind Database." Biomolecules 12, no. 7 (June 29, 2022): 919. http://dx.doi.org/10.3390/biom12070919.
Shi, Rongye, Zhaobin Mo, and Xuan Di. "Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 540–47. http://dx.doi.org/10.1609/aaai.v35i1.16132.
Qin, Songhai, Jianyi Liu, Xinping Yang, Yiyang Li, Lifeng Zhang, and Zhibin Liu. "Predicting Heavy Oil Production by Hybrid Data-Driven Intelligent Models." Mathematical Problems in Engineering 2021 (August 26, 2021): 1–15. http://dx.doi.org/10.1155/2021/5558623.
Gálvez, Antonio, Dammika Seneviratne, and Diego Galar. "Hybrid Model Development for HVAC System in Transportation." Technologies 9, no. 1 (March 5, 2021): 18. http://dx.doi.org/10.3390/technologies9010018.
Simmons, Joshua, and Kristen Splinter. "COMBINING DATA-DRIVEN AND NUMERICAL MODELLING APPROACHES TO STORM EROSION PREDICTION." Coastal Engineering Proceedings, no. 36v (December 28, 2020): 38. http://dx.doi.org/10.9753/icce.v36v.sediment.38.
Li, Zhe, Daniel B. Wright, Sara Q. Zhang, Dalia B. Kirschbaum, and Samantha H. Hartke. "Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall." Journal of Hydrometeorology 21, no. 12 (December 2020): 2759–76. http://dx.doi.org/10.1175/jhm-d-20-0041.1.
Liu, Di, Changchun Zou, Qianggong Song, Zhonghong Wan, and Haizhen Zhao. "A hybrid physics and machine learning approach for velocity prediction." Leading Edge 41, no. 6 (June 2022): 382–91. http://dx.doi.org/10.1190/tle41060382.1.
Huang, Xu, Guoqiang Zu, Qi Ding, Ran Wei, Yudong Wang, and Wei Wei. "An Online Control Method of Reactive Power and Voltage Based on Mechanism–Data Hybrid Drive Model Considering Source–Load Uncertainty." Energies 16, no. 8 (April 18, 2023): 3501. http://dx.doi.org/10.3390/en16083501.
Ibáñez, Rubén, Emmanuelle Abisset-Chavanne, David González, Jean-Louis Duval, Elias Cueto, and Francisco Chinesta. "Hybrid constitutive modeling: data-driven learning of corrections to plasticity models." International Journal of Material Forming 12, no. 4 (October 17, 2018): 717–25. http://dx.doi.org/10.1007/s12289-018-1448-x.
Ma, Lijing, Shiru Qu, Lijun Song, Zhiteng Zhang, and Jie Ren. "A Physics-Informed Generative Car-Following Model for Connected Autonomous Vehicles." Entropy 25, no. 7 (July 12, 2023): 1050. http://dx.doi.org/10.3390/e25071050.
Ahmed, Shady E., Omer San, Kursat Kara, Rami Younis, and Adil Rasheed. "Multifidelity computing for coupling full and reduced order models." PLOS ONE 16, no. 2 (February 11, 2021): e0246092. http://dx.doi.org/10.1371/journal.pone.0246092.
Wilhelm, Yannick, Peter Reimann, Wolfgang Gauchel, and Bernhard Mitschang. "Overview on hybrid approaches to fault detection and diagnosis: Combining data-driven, physics-based and knowledge-based models." Procedia CIRP 99 (2021): 278–83. http://dx.doi.org/10.1016/j.procir.2021.03.041.
Colombo, Daniele, Ersan Turkoglu, Weichang Li, Ernesto Sandoval-Curiel, and Diego Rovetta. "Physics-driven deep-learning inversion with application to transient electromagnetics." GEOPHYSICS 86, no. 3 (April 8, 2021): E209—E224. http://dx.doi.org/10.1190/geo2020-0760.1.
Sahar, Gul, Kamalrulnizam Abu Bakar, Sabit Rahim, Naveed Ali Khan Kaim Khani, and Tehmina Bibi. "Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey." Technologies 9, no. 4 (October 21, 2021): 76. http://dx.doi.org/10.3390/technologies9040076.
Ottersböck, Nicole, and Tim Jeske. "Potential of Cross-Operational Cooperation for Implementing Hybrid, Data-Driven Business Models." Procedia Computer Science 200 (2022): 852–57. http://dx.doi.org/10.1016/j.procs.2022.01.282.
Slater, Louise J., Louise Arnal, Marie-Amélie Boucher, Annie Y. Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, et al. "Hybrid forecasting: blending climate predictions with AI models." Hydrology and Earth System Sciences 27, no. 9 (May 15, 2023): 1865–89. http://dx.doi.org/10.5194/hess-27-1865-2023.
Aurand, Bastian, Esin Aktan, Kerstin Maria Schwind, Rajendra Prasad, Mirela Cerchez, Toma Toncian, and Oswald Willi. "A laser-driven droplet source for plasma physics applications." Laser and Particle Beams 38, no. 4 (September 11, 2020): 214–21. http://dx.doi.org/10.1017/s0263034620000282.
Liu, Binxiao, Qiuhong Tang, Gang Zhao, Liang Gao, Chaopeng Shen, and Baoxiang Pan. "Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang–Mekong River Basin." Water 14, no. 9 (April 29, 2022): 1429. http://dx.doi.org/10.3390/w14091429.
Yao, Shunyu, Guangyuan Kan, Changjun Liu, Jinbo Tang, Deqiang Cheng, Jian Guo, and Hu Jiang. "A Hybrid Theory-Driven and Data-Driven Modeling Method for Solving the Shallow Water Equations." Water 15, no. 17 (September 1, 2023): 3140. http://dx.doi.org/10.3390/w15173140.
Zhang, Wanwan, Jørn Vatn, and Adil Rasheed. "A review of failure prognostics for predictive maintenance of offshore wind turbines." Journal of Physics: Conference Series 2362, no. 1 (November 1, 2022): 012043. http://dx.doi.org/10.1088/1742-6596/2362/1/012043.
Alawsi, Mustafa A., Salah L. Zubaidi, Nabeel Saleem Saad Al-Bdairi, Nadhir Al-Ansari, and Khalid Hashim. "Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing." Hydrology 9, no. 7 (June 26, 2022): 115. http://dx.doi.org/10.3390/hydrology9070115.
Jin, Xue-Bo, Ruben Jonhson Robert Jeremiah, Ting-Li Su, Yu-Ting Bai, and Jian-Lei Kong. "The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods." Sensors 21, no. 6 (March 16, 2021): 2085. http://dx.doi.org/10.3390/s21062085.
Yucesan, Yigit Anil, and Felipe Viana. "Hybrid Model for Wind Turbine Main Bearing Fatigue with Uncertainty in Grease Observations." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 14. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1139.
Gharbia, Salem, Khurram Riaz, Iulia Anton, Gabor Makrai, Laurence Gill, Leo Creedon, Marion McAfee, Paul Johnston, and Francesco Pilla. "Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale." Sustainability 14, no. 7 (March 29, 2022): 4037. http://dx.doi.org/10.3390/su14074037.
Laufer-Goldshtein, Bracha, Ronen Talmon, and Sharon Gannot. "A Hybrid Approach for Speaker Tracking Based on TDOA and Data-Driven Models." IEEE/ACM Transactions on Audio, Speech, and Language Processing 26, no. 4 (April 2018): 725–35. http://dx.doi.org/10.1109/taslp.2018.2790707.
Zhu, Senlin, Marijana Hadzima-Nyarko, Ang Gao, Fangfang Wang, Jingxiu Wu, and Shiqiang Wu. "Two hybrid data-driven models for modeling water-air temperature relationship in rivers." Environmental Science and Pollution Research 26, no. 12 (March 20, 2019): 12622–30. http://dx.doi.org/10.1007/s11356-019-04716-y.
ElGhawi, Reda, Basil Kraft, Christian Reimers, Markus Reichstein, Marco Körner, Pierre Gentine, and Alexander J. Winkler. "Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning." Environmental Research Letters 18, no. 3 (March 1, 2023): 034039. http://dx.doi.org/10.1088/1748-9326/acbbe0.
Zakwan, Mohammad, and Majid Niazkar. "A Comparative Analysis of Data-Driven Empirical and Artificial Intelligence Models for Estimating Infiltration Rates." Complexity 2021 (May 4, 2021): 1–13. http://dx.doi.org/10.1155/2021/9945218.
Vidyarthi, Vikas Kumar, and Ashu Jain. "Incorporating non-uniformity and non-linearity of hydrologic and catchment characteristics in rainfall–runoff modeling using conceptual, data-driven, and hybrid techniques." Journal of Hydroinformatics 24, no. 2 (February 3, 2022): 350–66. http://dx.doi.org/10.2166/hydro.2022.088.
RATH, S., P. P. SENGUPTA, A. P. SINGH, A. K. MARIK, and P. TALUKDAR. "MATHEMATICAL-ARTIFICIAL NEURAL NETWORK HYBRID MODEL TO PREDICT ROLL FORCE DURING HOT ROLLING OF STEEL." International Journal of Computational Materials Science and Engineering 02, no. 01 (March 2013): 1350004. http://dx.doi.org/10.1142/s2047684113500048.
Althoff, Daniel, Helizani Couto Bazame, and Jessica Garcia Nascimento. "Untangling hybrid hydrological models with explainable artificial intelligence." H2Open Journal 4, no. 1 (January 1, 2021): 13–28. http://dx.doi.org/10.2166/h2oj.2021.066.
Zhao, Dengfeng, Haiyang Li, Fang Zhou, Yudong Zhong, Guosheng Zhang, Zhaohui Liu, and Junjian Hou. "Research Progress on Data-Driven Methods for Battery States Estimation of Electric Buses." World Electric Vehicle Journal 14, no. 6 (June 2, 2023): 145. http://dx.doi.org/10.3390/wevj14060145.
Rodrigues, Pedro Miguel, Pedro Ribeiro, and Freni Kekhasharú Tavaria. "Distinction of Different Colony Types by a Smart-Data-Driven Tool." Bioengineering 10, no. 1 (December 24, 2022): 26. http://dx.doi.org/10.3390/bioengineering10010026.
Zhao, Dengfeng, Haiyang Li, Junjian Hou, Pengliang Gong, Yudong Zhong, Wenbin He, and Zhijun Fu. "A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption." Energies 16, no. 14 (July 9, 2023): 5258. http://dx.doi.org/10.3390/en16145258.
Nguyen, Huu-Linh, Sang-Min Lee, and Sangseok Yu. "A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell." Energies 16, no. 12 (June 16, 2023): 4772. http://dx.doi.org/10.3390/en16124772.
Camargo, Manuel, Marlon Dumas, and Oscar González-Rojas. "Discovering generative models from event logs: data-driven simulation vs deep learning." PeerJ Computer Science 7 (July 12, 2021): e577. http://dx.doi.org/10.7717/peerj-cs.577.
Gálvez, Antonio, Alberto Diez-Olivan, Dammika Seneviratne, and Diego Galar. "Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach." Sustainability 13, no. 12 (June 16, 2021): 6828. http://dx.doi.org/10.3390/su13126828.
Hwang, Jun Kwon, Patrick Nzivugira Duhirwe, Geun Young Yun, Sukho Lee, Hyeongjoon Seo, Inhan Kim, and Mat Santamouris. "A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps." Sustainability 12, no. 7 (April 6, 2020): 2914. http://dx.doi.org/10.3390/su12072914.
Derouiche, Khouloud, Sevan Garois, Victor Champaney, Monzer Daoud, Khalil Traidi, and Francisco Chinesta. "Data-Driven Modeling for Multiphysics Parametrized Problems-Application to Induction Hardening Process." Metals 11, no. 5 (April 29, 2021): 738. http://dx.doi.org/10.3390/met11050738.
Mekonnen, Balew A., Alireza Nazemi, Kerry A. Mazurek, Amin Elshorbagy, and Gordon Putz. "Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models." Hydrological Sciences Journal 60, no. 9 (June 22, 2015): 1473–89. http://dx.doi.org/10.1080/02626667.2014.935778.
Liang, Ruihua, Weifeng Liu, Sakdirat Kaewunruen, Hougui Zhang, and Zongzhen Wu. "Classification of External Vibration Sources through Data-Driven Models Using Hybrid CNNs and LSTMs." Structural Control and Health Monitoring 2023 (March 13, 2023): 1–18. http://dx.doi.org/10.1155/2023/1900447.