Zeitschriftenartikel zum Thema „Physics-guided Machine Learning“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit Top-50 Zeitschriftenartikel für die Forschung zum Thema "Physics-guided Machine Learning" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Sehen Sie die Zeitschriftenartikel für verschiedene Spezialgebieten durch und erstellen Sie Ihre Bibliographie auf korrekte Weise.
Pawar, Suraj, Omer San, Burak Aksoylu, Adil Rasheed und Trond Kvamsdal. „Physics guided machine learning using simplified theories“. Physics of Fluids 33, Nr. 1 (01.01.2021): 011701. http://dx.doi.org/10.1063/5.0038929.
Der volle Inhalt der QuellePawar, Suraj, Omer San, Burak Aksoylu, Adil Rasheed und Trond Kvamsdal. „Physics guided machine learning using simplified theories“. Physics of Fluids 33, Nr. 1 (01.01.2021): 011701. http://dx.doi.org/10.1063/5.0038929.
Der volle Inhalt der QuelleJørgensen, Ulrik, Pauline Røstum Belingmo, Brian Murray, Svein Peder Berge und Armin Pobitzer. „Ship route optimization using hybrid physics-guided machine learning“. Journal of Physics: Conference Series 2311, Nr. 1 (01.07.2022): 012037. http://dx.doi.org/10.1088/1742-6596/2311/1/012037.
Der volle Inhalt der QuelleWinter, B., J. Schilling und A. Bardow. „Physics‐guided machine learning to predict activity coefficients from SMILES“. Chemie Ingenieur Technik 94, Nr. 9 (25.08.2022): 1320. http://dx.doi.org/10.1002/cite.202255153.
Der volle Inhalt der QuelleAhmed, Shady E., Omer San, Adil Rasheed, Traian Iliescu und Alessandro Veneziani. „Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling“. SIAM Journal on Scientific Computing 45, Nr. 3 (06.06.2023): B283—B313. http://dx.doi.org/10.1137/22m1496360.
Der volle Inhalt der QuelleBanyay, Gregory A., und Andrew S. Wixom. „Predictive capability assessment for physics-guided learning of vortex-induced vibrations“. Journal of the Acoustical Society of America 152, Nr. 4 (Oktober 2022): A48. http://dx.doi.org/10.1121/10.0015496.
Der volle Inhalt der QuelleJia, Xiaowei. „Physics-guided machine learning: A new paradigm for scientific knowledge discovery“. Microscopy and Microanalysis 27, S1 (30.07.2021): 1344–45. http://dx.doi.org/10.1017/s1431927621005018.
Der volle Inhalt der QuelleYu, Yang, Houpu Yao und Yongming Liu. „Structural dynamics simulation using a novel physics-guided machine learning method“. Engineering Applications of Artificial Intelligence 96 (November 2020): 103947. http://dx.doi.org/10.1016/j.engappai.2020.103947.
Der volle Inhalt der QuellePawar, Suraj, Omer San, Aditya Nair, Adil Rasheed und Trond Kvamsdal. „Model fusion with physics-guided machine learning: Projection-based reduced-order modeling“. Physics of Fluids 33, Nr. 6 (Juni 2021): 067123. http://dx.doi.org/10.1063/5.0053349.
Der volle Inhalt der QuelleHoerig, Cameron, Jamshid Ghaboussi und Michael F. Insana. „Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging“. Physics in Medicine & Biology 65, Nr. 6 (20.03.2020): 065011. http://dx.doi.org/10.1088/1361-6560/ab7505.
Der volle Inhalt der QuelleTetali, Harsha Vardhan, und Joel Harley. „A physics-informed machine learning based dispersion curve estimation for non-homogeneous media“. Journal of the Acoustical Society of America 152, Nr. 4 (Oktober 2022): A239. http://dx.doi.org/10.1121/10.0016136.
Der volle Inhalt der QuelleTeurtrie, Adrien, Nathanaël Perraudin, Thomas Holvoet, Hui Chen, Duncan T. L. Alexander, Guillaume Obozinski und Cécile Hébert. „Physics-Guided Machine Learning for the Analysis of Low SNR STEM-EDXS Data“. Microscopy and Microanalysis 28, S1 (22.07.2022): 2978–79. http://dx.doi.org/10.1017/s1431927622011163.
Der volle Inhalt der QuelleBuster, Grant, Mike Bannister, Aron Habte, Dylan Hettinger, Galen Maclaurin, Michael Rossol, Manajit Sengupta und Yu Xie. „Physics-guided machine learning for improved accuracy of the National Solar Radiation Database“. Solar Energy 232 (Januar 2022): 483–92. http://dx.doi.org/10.1016/j.solener.2022.01.004.
Der volle Inhalt der QuelleJia, Xiaowei, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach und Vipin Kumar. „Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles“. ACM/IMS Transactions on Data Science 2, Nr. 3 (17.05.2021): 1–26. http://dx.doi.org/10.1145/3447814.
Der volle Inhalt der QuelleHagmeyer, Simon, Peter Zeiler und Marco F. Huber. „On the Integration of Fundamental Knowledge about Degradation Processes into Data-Driven Diagnostics and Prognostics Using Theory-Guided Data Science“. PHM Society European Conference 7, Nr. 1 (29.06.2022): 156–65. http://dx.doi.org/10.36001/phme.2022.v7i1.3352.
Der volle Inhalt der QuelleZeng, Shi, und Dechang Pi. „Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning“. Sensors 23, Nr. 10 (22.05.2023): 4969. http://dx.doi.org/10.3390/s23104969.
Der volle Inhalt der QuelleGurgen, Anil, und Nam T. Dinh. „Development and assessment of a reactor system prognosis model with physics-guided machine learning“. Nuclear Engineering and Design 398 (November 2022): 111976. http://dx.doi.org/10.1016/j.nucengdes.2022.111976.
Der volle Inhalt der QuellePiccione, A., J. W. Berkery, S. A. Sabbagh und Y. Andreopoulos. „Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas“. Nuclear Fusion 60, Nr. 4 (18.03.2020): 046033. http://dx.doi.org/10.1088/1741-4326/ab7597.
Der volle Inhalt der QuelleJurj, Sorin Liviu, Dominik Grundt, Tino Werner, Philipp Borchers, Karina Rothemann und Eike Möhlmann. „Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning“. Energies 14, Nr. 22 (12.11.2021): 7572. http://dx.doi.org/10.3390/en14227572.
Der volle Inhalt der QuelleLiang, Yu, und Dalei Wu. „Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction“. Journal of Systemics, Cybernetics and Informatics 20, Nr. 5 (Oktober 2022): 148–59. http://dx.doi.org/10.54808/jsci.20.05.148.
Der volle Inhalt der QuelleCheng, Baolian, und Paul A. Bradley. „What Machine Learning Can and Cannot Do for Inertial Confinement Fusion“. Plasma 6, Nr. 2 (01.06.2023): 334–44. http://dx.doi.org/10.3390/plasma6020023.
Der volle Inhalt der QuelleQin, Yue, Changyu Su, Dongdong Chu, Jicai Zhang und Jinbao Song. „A Review of Application of Machine Learning in Storm Surge Problems“. Journal of Marine Science and Engineering 11, Nr. 9 (01.09.2023): 1729. http://dx.doi.org/10.3390/jmse11091729.
Der volle Inhalt der QuelleCunha, Barbara Zaparoli, Abdel-Malek Zine, Mohamed Ichchou, Christophe Droz und Stéphane Foulard. „On Machine-Learning-Driven Surrogates for Sound Transmission Loss Simulations“. Applied Sciences 12, Nr. 21 (23.10.2022): 10727. http://dx.doi.org/10.3390/app122110727.
Der volle Inhalt der QuelleWu, Xiaoqin, und Yipei Wang. „A physics-based machine learning approach for modeling the complex reflection coefficients of metal nanowires“. Nanotechnology 33, Nr. 20 (21.02.2022): 205701. http://dx.doi.org/10.1088/1361-6528/ac512e.
Der volle Inhalt der QuelleNguyen, Cong Tien, Selda Oterkus und Erkan Oterkus. „A physics-guided machine learning model for two-dimensional structures based on ordinary state-based peridynamics“. Theoretical and Applied Fracture Mechanics 112 (April 2021): 102872. http://dx.doi.org/10.1016/j.tafmec.2020.102872.
Der volle Inhalt der QuelleWiedemann, Arthur, Christopher Fuller und Kyle Pascioni. „Constructing a physics-guided machine learning neural network to predict tonal noise emitted by a propeller“. INTER-NOISE and NOISE-CON Congress and Conference Proceedings 264, Nr. 1 (24.06.2022): 151–62. http://dx.doi.org/10.3397/nc-2022-709.
Der volle Inhalt der QuelleLiang, Lin, und Ting Lei. „Machine-Learning-Enabled Automatic Sonic Shear Processing“. Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 62, Nr. 3 (01.06.2021): 282–95. http://dx.doi.org/10.30632/pjv62n3-2021a3.
Der volle Inhalt der QuelleGulick, Walter. „“Rules of Rightness” and the Evolutionary Emergence of Purpose“. Tradition and Discovery: The Polanyi Society Periodical 49, Nr. 1 (2023): 21–26. http://dx.doi.org/10.5840/traddisc20234914.
Der volle Inhalt der QuelleKo, Hyunwoong, Yan Lu, Zhuo Yang, Ndeye Y. Ndiaye und Paul Witherell. „A framework driven by physics-guided machine learning for process-structure-property causal analytics in additive manufacturing“. Journal of Manufacturing Systems 67 (April 2023): 213–28. http://dx.doi.org/10.1016/j.jmsy.2022.09.010.
Der volle Inhalt der QuelleZhang, Junru, Yang Liu, Durga Chandra Sekhar.P, Manjot Singh, Yuxin Tong, Ezgi Kucukdeger, Hu Young Yoon et al. „Rapid, autonomous high-throughput characterization of hydrogel rheological properties via automated sensing and physics-guided machine learning“. Applied Materials Today 30 (Februar 2023): 101720. http://dx.doi.org/10.1016/j.apmt.2022.101720.
Der volle Inhalt der QuelleCao, Bin, Shuang Yang, Ankang Sun, Ziqiang Dong und Tong-Yi Zhang. „Domain knowledge-guided interpretive machine learning: formula discovery for the oxidation behavior of ferritic-martensitic steels in supercritical water“. Journal of Materials Informatics 2, Nr. 2 (2022): 4. http://dx.doi.org/10.20517/jmi.2022.04.
Der volle Inhalt der QuelleRai, Rahul, und Chandan K. Sahu. „Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus“. IEEE Access 8 (2020): 71050–73. http://dx.doi.org/10.1109/access.2020.2987324.
Der volle Inhalt der QuelleDhara, Arnab, und Mrinal K. Sen. „Physics-guided deep autoencoder to overcome the need for a starting model in full-waveform inversion“. Leading Edge 41, Nr. 6 (Juni 2022): 375–81. http://dx.doi.org/10.1190/tle41060375.1.
Der volle Inhalt der QuelleFadziso, Takudzwa. „Enhancing Predictions in Ungauged Basins Using Machine Learning to Its Full Potential“. Asian Journal of Applied Science and Engineering 8, Nr. 1 (05.05.2019): 35–50. http://dx.doi.org/10.18034/ajase.v8i1.10.
Der volle Inhalt der QuelleSuryani, Dewi, Mohamad Nur und Wasis Wasis. „PENGEMBANGAN PROTOTIPE PERANGKAT PEMBELAJARAN FISIKA SMK MODEL INKUIRI TERBIMBING MATERI CERMIN UNTUK MELATIHKAN KETERAMPILAN BERPIKIR KRITIS“. JPPS (Jurnal Penelitian Pendidikan Sains) 6, Nr. 1 (31.01.2017): 1175. http://dx.doi.org/10.26740/jpps.v6n1.p1175-1183.
Der volle Inhalt der QuelleBiswas, Reetam, Mrinal K. Sen, Vishal Das und Tapan Mukerji. „Prestack and poststack inversion using a physics-guided convolutional neural network“. Interpretation 7, Nr. 3 (01.08.2019): SE161—SE174. http://dx.doi.org/10.1190/int-2018-0236.1.
Der volle Inhalt der QuelleXie, Yazhou, Majid Ebad Sichani, Jamie E. Padgett und Reginald DesRoches. „The promise of implementing machine learning in earthquake engineering: A state-of-the-art review“. Earthquake Spectra 36, Nr. 4 (03.06.2020): 1769–801. http://dx.doi.org/10.1177/8755293020919419.
Der volle Inhalt der QuelleZhang, Hongliang, Kristopher A. Innanen und David W. Eaton. „Inversion for Shear-Tensile Focal Mechanisms Using an Unsupervised Physics-Guided Neural Network“. Seismological Research Letters 92, Nr. 4 (24.03.2021): 2282–94. http://dx.doi.org/10.1785/0220200420.
Der volle Inhalt der QuelleGopalakrishnan Meena, Muralikrishnan, Amir K. Ziabari, Singanallur V. Venkatakrishnan, Isaac R. Lyngaas, Matthew R. Norman, Balint Joo, Thomas L. Beck, Charles A. Bouman, Anuj J. Kapadia und Xiao Wang. „Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications“. Electronic Imaging 35, Nr. 11 (16.01.2023): 228–1. http://dx.doi.org/10.2352/ei.2023.35.11.hpci-228.
Der volle Inhalt der QuelleNaser, M. Z. „Mapping functions: A physics-guided, data-driven and algorithm-agnostic machine learning approach to discover causal and descriptive expressions of engineering phenomena“. Measurement 185 (November 2021): 110098. http://dx.doi.org/10.1016/j.measurement.2021.110098.
Der volle Inhalt der QuelleSeyyedi, Azra, Mahdi Bohlouli und SeyedEhsan Nedaaee Oskoee. „Machine Learning and Physics: A Survey of Integrated Models“. ACM Computing Surveys, 03.08.2023. http://dx.doi.org/10.1145/3611383.
Der volle Inhalt der QuelleGarpelli, Lucas Nogueira, Diogo Stuani Alves, Katia Lucchesi Cavalca und Helio Fiori de Castro. „Physics-guided neural networks applied in rotor unbalance problems“. Structural Health Monitoring, 10.04.2023, 147592172311630. http://dx.doi.org/10.1177/14759217231163081.
Der volle Inhalt der QuelleZhao, Luanxiao, Jingyu Liu, Minghui Xu, Zhenyu Zhu, Yuanyuan Chen und Jianhua Geng. „Rock Physics guided machine learning for shear sonic log prediction“. GEOPHYSICS, 11.10.2023, 1–71. http://dx.doi.org/10.1190/geo2023-0152.1.
Der volle Inhalt der QuelleBraiek, Houssem Ben, Thomas Reid und Foutse Khomh. „Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation“. IEEE Transactions on Reliability, 2022, 1–15. http://dx.doi.org/10.1109/tr.2022.3196272.
Der volle Inhalt der QuelleChen, Jie, und Yongming Liu. „Probabilistic physics-guided machine learning for fatigue data analysis“. Expert Systems with Applications, November 2020, 114316. http://dx.doi.org/10.1016/j.eswa.2020.114316.
Der volle Inhalt der QuelleNakazawa, Ryota, Yuki Minamoto, Nakamasa Inoue und Mamoru Tanahashi. „Species reaction rate modelling based on physics-guided machine learning“. Combustion and Flame, August 2021, 111696. http://dx.doi.org/10.1016/j.combustflame.2021.111696.
Der volle Inhalt der QuelleGreis, Noel P., Monica L. Nogueira, Sambit Bhattacharya, Catherine Spooner und Tony Schmitz. „Stability modeling for chatter avoidance in self-aware machining: an application of physics-guided machine learning“. Journal of Intelligent Manufacturing, 09.11.2022. http://dx.doi.org/10.1007/s10845-022-01999-w.
Der volle Inhalt der QuelleGuo, Lulu, Jin Ye und Bowen Yang. „Cyber-Attack Detection for Electric Vehicles Using Physics-Guided Machine Learning“. IEEE Transactions on Transportation Electrification, 2020, 1. http://dx.doi.org/10.1109/tte.2020.3044524.
Der volle Inhalt der QuelleAbu-Mualla, Mohammad, und Jida Huang. „Inverse Design of 3D Cellular Materials with Physics-Guided Machine Learning“. Materials & Design, Juli 2023, 112103. http://dx.doi.org/10.1016/j.matdes.2023.112103.
Der volle Inhalt der QuelleChen, Shengyu, Nasrin Kalanat, Yiqun Xie, Sheng Li, Jacob A. Zwart, Jeffrey M. Sadler, Alison P. Appling, Samantha K. Oliver, Jordan S. Read und Xiaowei Jia. „Physics-guided machine learning from simulated data with different physical parameters“. Knowledge and Information Systems, 31.03.2023. http://dx.doi.org/10.1007/s10115-023-01864-z.
Der volle Inhalt der Quelle