Добірка наукової літератури з теми "Hybrid physics-data driven models"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Hybrid physics-data driven models".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Hybrid physics-data driven models"
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.
Повний текст джерелаGroves, 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.
Повний текст джерелаJørgensen, 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.
Повний текст джерелаSun, 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.
Повний текст джерелаYun, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаBelov, 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.
Повний текст джерелаFernandes, 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.
Повний текст джерелаAl 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.
Повний текст джерелаCain, 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.
Повний текст джерелаДисертації з теми "Hybrid physics-data driven models"
Hussain, Mukhtar. "Data-driven discovery of mode switching conditions to create hybrid models of cyber-physical systems." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235043/1/Mukhtar_Hussain_Thesis.pdf.
Повний текст джерелаAjib, Balsam. "Data-driven building thermal modeling using system identification for hybrid systems." Thesis, Ecole nationale supérieure Mines-Télécom Lille Douai, 2018. http://www.theses.fr/2018MTLD0006/document.
Повний текст джерелаThe building sector is a major energy consumer, therefore, a framework of actions has been decided on by countries worldwide to limit its impact. For implementing such actions, the availability of models providing an accurate description of the thermal behavior of buildings is essential. For this purpose, this thesis proposes the application of a new data-driven technique for modeling the thermal behavior of buildings based on a hybrid system approach. Hybrid systems exhibit both continuous and discrete dynamics. This choice is motivated by the fact that a building is a complex system characterized by nonlinear phenomena and the occurrence of different events. We use a PieceWise AutoRegressive eXogeneous inputs (PWARX) model for the identification of hybrid systems. It is a collection of sub-models where each sub-model is an ARX equation representing a certain configuration in the building characterized by its own dynamics. This thesis starts with a state-of-the-art on building thermal modeling. Then, the choice of a hybrid system approach is motivated by a mathematical interpretation based on the equations derived from an RC thermal circuit of a building zone. This is followed by a brief background about hybrid system identification and a detailed description of the PWARX methodology. For the prediction phase, it is shown how to use the Support Vector Machine (SVM) technique to classify new data to the right sub-model. Then, it is shown how to integrate these models in a hybrid control loop to estimate the gain in the energy performance for a building after insulation work. The performance of the proposed technique is validated using data collected from various test cases
CIPOLLINI, FRANCESCA. "Data-Driven and Hybrid Methods for Naval Applications." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/989847.
Повний текст джерелаSELICATI, VALERIA. "Innovative thermodynamic hybrid model-based and data-driven techniques for real time manufacturing sustainability assessment." Doctoral thesis, Università degli studi della Basilicata, 2022. http://hdl.handle.net/11563/157566.
Повний текст джерелаMIGLIANTI, LEONARDO PIETRO. "Modelling of the cavitating propeller noise by means of semi-empirical and data driven approaches." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1004161.
Повний текст джерелаEker, Ömer F. "A hybrid prognostic methodology and its application to well-controlled engineering systems." Thesis, Cranfield University, 2015. http://dspace.lib.cranfield.ac.uk/handle/1826/9269.
Повний текст джерелаWileman, Andrew John. "An investigation into the prognosis of electromagnetic relays." Thesis, Cranfield University, 2016. http://dspace.lib.cranfield.ac.uk/handle/1826/13665.
Повний текст джерелаKleman, Björn, and Henrik Lindgren. "Evaluation of model-based fault diagnosis combining physical insights and neural networks applied to an exhaust gas treatment system case study." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176650.
Повний текст джерелаMesbahi, Tedjani. "Influence des stratégies de gestion d’une source hybride de véhicule électrique sur son dimensionnement et sa durée de vie par intégration d’un modèle multi-physique." Thesis, Ecole centrale de Lille, 2016. http://www.theses.fr/2016ECLI0004/document.
Повний текст джерелаThis thesis contributes to the improvement of hybrid embedded source performances supplies an electric vehicle. The studied solution is composed of Li-ion batteries and supercapacitors hybridization, with an aim to achieve improved performances in terms of weight and lifetime over traditional solutions. Our main goal is to take the best advantage of new energy management strategies of the hybrid embedded source and quantify obtained improvements. A multi-physic model including electric, thermal and aging behaviors is developed and integrated into the algorithm of energy management in order to evaluate the gradual degradation of storage components performances during driving cycles and implemented control strategy. New energy management strategies intended to act on the lifetime of hybrid embedded source have been evaluated. Their impact on the performances of the source in terms of weight, cost and lifetime has been quantified and clearly shows that it is possible to make better use of hybrid embedded source thanks to a good power sharing, thus opening the way to new approaches of energy management for these systems
Rautela, Mahindra Singh. "Hybrid Physics-Data Driven Models for the Solution of Mechanics Based Inverse Problems." Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6123.
Повний текст джерелаКниги з теми "Hybrid physics-data driven models"
Perez, Gerald Augusto Corzo. Hybrid Models for Hydrological Forecasting : Integration of Data-Driven and Conceptual Modelling Techniques: UNESCO-IHE PhD Thesis. Taylor & Francis Group, 2017.
Знайти повний текст джерелаLægreid, Per. New Public Management. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.159.
Повний текст джерелаЧастини книг з теми "Hybrid physics-data driven models"
Araghinejad, Shahab. "Hybrid Models and Multi-model Data Fusion." In Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering, 253–65. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7506-0_8.
Повний текст джерелаAbdulali, Arsen, and Seokhee Jeon. "Haptic Software Design." In Springer Series on Touch and Haptic Systems, 537–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04536-3_12.
Повний текст джерелаTraini, Emiliano, Giulia Bruno, and Franco Lombardi. "Design of a Physics-Based and Data-Driven Hybrid Model for Predictive Maintenance." In Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems, 536–43. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85914-5_57.
Повний текст джерелаValeti, Bhavana, and Shamim N. Pakzad. "Uncertainty Propagation in a Hybrid Data-Driven and Physics-Based Submodeling Method for Refined Response Estimation." In Model Validation and Uncertainty Quantification, Volume 3, 349–59. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0_38.
Повний текст джерелаWilde, A. S., S. Gellrich, M. Mennenga, T. Abraham, and C. Herrmann. "Data-Driven Business Models for Life Cycle Technologies: Exemplary Planning for Hybrid Components." In Lecture Notes in Production Engineering, 488–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78424-9_54.
Повний текст джерелаPohlkötter, Fabian J., Dominik Straubinger, Alexander M. Kuhn, Christian Imgrund, and William Tekouo. "Unlocking the Potential of Digital Twins." In Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains, 190–99. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27933-1_18.
Повний текст джерелаHasanov, Fakhri J., Frederick L. Joutz, Jeyhun I. Mikayilov, and Muhammad Javid. "KGEMM Methodology." In SpringerBriefs in Economics, 21–24. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-12275-0_4.
Повний текст джерелаCamargo, Manuel, Marlon Dumas, and Oscar González-Rojas. "Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning." In Advanced Information Systems Engineering, 55–71. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07472-1_4.
Повний текст джерелаPrecup, Radu-Emil, Raul-Cristian Roman, and Ali Safaei. "Hybrid Model-Free and Model-Free Adaptive Fuzzy Controllers." In Data-Driven Model-Free Controllers, 259–342. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003143444-7.
Повний текст джерелаPrecup, Radu-Emil, Raul-Cristian Roman, and Ali Safaei. "Hybrid Model-Free and Model-Free Adaptive Virtual Reference Feedback Tuning Controllers." In Data-Driven Model-Free Controllers, 211–57. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003143444-6.
Повний текст джерелаТези доповідей конференцій з теми "Hybrid physics-data driven models"
Chinesta, F., E. Cueto, and J. Duval. "Physics-based and data-driven hybrid modeling: when data enrich models and models render data smarter." In 9th edition of the International Conference on Computational Methods for Coupled Problems in Science and Engineering. CIMNE, 2021. http://dx.doi.org/10.23967/coupled.2021.068.
Повний текст джерелаZalavadia, Hardikkumar, Utkarsh Sinha, Prithvi Singh, and Sathish Sankaran. "Discovery of Unconventional Reservoir Flow Physics for Production Forecasting Through Hybrid Data-Driven and Physics Models." In SPE Western Regional Meeting. SPE, 2023. http://dx.doi.org/10.2118/213004-ms.
Повний текст джерелаErge, Oney, and Eric van Oort. "Hybrid Physics-Based and Data-Driven Modeling for Improved Standpipe Pressure Prediction." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/204094-ms.
Повний текст джерелаKovalev, Dmitry, Sergey Safonov, Klemens Katterbauer, and Alberto Marsala. "Hybrid Physics-Constrained and Data-Driven Approach for Interwell Saturation Estimation from Well Logs." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207457-ms.
Повний текст джерелаZalavadia, Hardikkumar, Metin Gokdemir, Utkarsh Sinha, Prithvi Singh, and Sathish Sankaran. "Real Time Artificial Lift Timing and Selection Using Hybrid Data-Driven and Physics Models." In SPE Western Regional Meeting. SPE, 2023. http://dx.doi.org/10.2118/213040-ms.
Повний текст джерелаNagao, Masahiro, Wenyue Sun, and Sathish Sankaran. "Data-Driven Discovery of Physics for Reservoir Surveillance." In SPE Western Regional Meeting. SPE, 2022. http://dx.doi.org/10.2118/209300-ms.
Повний текст джерелаStoffel, Phillip, Charlotte Loffler, Steffen Eser, Alexander Kumpel, and Dirk Muller. "Combining Data-driven and Physics-based Process Models for Hybrid Model Predictive Control of Building Energy Systems." In 2022 30th Mediterranean Conference on Control and Automation (MED). IEEE, 2022. http://dx.doi.org/10.1109/med54222.2022.9837277.
Повний текст джерелаMichael, Andreas. "A Hybrid Data-Driven/Physics-Based Approach for Near-Wellbore Hydraulic Fracture Modeling." In SPE Hydraulic Fracturing Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212355-ms.
Повний текст джерелаSinha, Utkarsh, Hardikkumar Zalavadia, and Sathish Sankaran. "Physics Guided Data Driven Model to Forecast Production Rates in Liquid Wells." In SPE Oklahoma City Oil and Gas Symposium. SPE, 2023. http://dx.doi.org/10.2118/213103-ms.
Повний текст джерелаKaneko, Tatsuya, Ryota Wada, Masahiko Ozaki, and Tomoya Inoue. "Combining Physics-Based and Data-Driven Models for Estimation of WOB During Ultra-Deep Ocean Drilling." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-78229.
Повний текст джерелаЗвіти організацій з теми "Hybrid physics-data driven models"
Dargazany, Roozbeh, Emad Poshtan, Hamid Mohammadi, William Mars, Mamoon Shaafaey, and Yang Chen. A Hybrid Physics-Based, Data-Driven Approach to Model Damage Accumulation in Corrosion of Polymeric Adhesives. Office of Scientific and Technical Information (OSTI), December 2023. http://dx.doi.org/10.2172/1961542.
Повний текст джерелаBalasubramaniam, K. S., D. C. Norquist, T. Henry, and M. Kirk. Physics of Solar Flares and Development of Statistical and Data Driven Models. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada591356.
Повний текст джерелаSelvaraju, Ragul, Hari Shankar, and Hariharan Sankarasubramanian. Metamodel Generation for Frontal Crash Scenario of a Passenger Car. SAE International, September 2020. http://dx.doi.org/10.4271/2020-28-0504.
Повний текст джерелаSelvaraju, Ragul, Hari Shankar, and Hariharan Sankarasubramanian. Metamodel Generation for Frontal Crash Scenario of a Passenger Car. SAE International, September 2020. http://dx.doi.org/10.4271/2020-28-0504.
Повний текст джерелаPettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41034.
Повний текст джерелаSeale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41282.
Повний текст джерелаSeale, Maria, R. Salter, Natàlia Garcia-Reyero,, and Alicia Ruvinsky. A fuzzy epigenetic model for representing degradation in engineered systems. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45582.
Повний текст джерелаRuvinsky, Alicia, Maria Seale, R. Salter, and Natàlia Garcia-Reyero. An ontology for an epigenetics approach to prognostics and health management. Engineer Research and Development Center (U.S.), March 2023. http://dx.doi.org/10.21079/11681/46632.
Повний текст джерела