Artykuły w czasopismach na temat „Physics-guided Machine Learning”
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Pawar, Suraj, Omer San, Burak Aksoylu, Adil Rasheed i Trond Kvamsdal. "Physics guided machine learning using simplified theories". Physics of Fluids 33, nr 1 (1.01.2021): 011701. http://dx.doi.org/10.1063/5.0038929.
Pełny tekst źródłaPawar, Suraj, Omer San, Burak Aksoylu, Adil Rasheed i Trond Kvamsdal. "Physics guided machine learning using simplified theories". Physics of Fluids 33, nr 1 (1.01.2021): 011701. http://dx.doi.org/10.1063/5.0038929.
Pełny tekst źródłaJørgensen, Ulrik, Pauline Røstum Belingmo, Brian Murray, Svein Peder Berge i Armin Pobitzer. "Ship route optimization using hybrid physics-guided machine learning". Journal of Physics: Conference Series 2311, nr 1 (1.07.2022): 012037. http://dx.doi.org/10.1088/1742-6596/2311/1/012037.
Pełny tekst źródłaWinter, B., J. Schilling i 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.
Pełny tekst źródłaAhmed, Shady E., Omer San, Adil Rasheed, Traian Iliescu i Alessandro Veneziani. "Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling". SIAM Journal on Scientific Computing 45, nr 3 (6.06.2023): B283—B313. http://dx.doi.org/10.1137/22m1496360.
Pełny tekst źródłaBanyay, Gregory A., i Andrew S. Wixom. "Predictive capability assessment for physics-guided learning of vortex-induced vibrations". Journal of the Acoustical Society of America 152, nr 4 (październik 2022): A48. http://dx.doi.org/10.1121/10.0015496.
Pełny tekst źródłaJia, 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.
Pełny tekst źródłaYu, Yang, Houpu Yao i Yongming Liu. "Structural dynamics simulation using a novel physics-guided machine learning method". Engineering Applications of Artificial Intelligence 96 (listopad 2020): 103947. http://dx.doi.org/10.1016/j.engappai.2020.103947.
Pełny tekst źródłaPawar, Suraj, Omer San, Aditya Nair, Adil Rasheed i Trond Kvamsdal. "Model fusion with physics-guided machine learning: Projection-based reduced-order modeling". Physics of Fluids 33, nr 6 (czerwiec 2021): 067123. http://dx.doi.org/10.1063/5.0053349.
Pełny tekst źródłaHoerig, Cameron, Jamshid Ghaboussi i 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.
Pełny tekst źródłaTetali, Harsha Vardhan, i 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 (październik 2022): A239. http://dx.doi.org/10.1121/10.0016136.
Pełny tekst źródłaTeurtrie, Adrien, Nathanaël Perraudin, Thomas Holvoet, Hui Chen, Duncan T. L. Alexander, Guillaume Obozinski i 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.
Pełny tekst źródłaBuster, Grant, Mike Bannister, Aron Habte, Dylan Hettinger, Galen Maclaurin, Michael Rossol, Manajit Sengupta i Yu Xie. "Physics-guided machine learning for improved accuracy of the National Solar Radiation Database". Solar Energy 232 (styczeń 2022): 483–92. http://dx.doi.org/10.1016/j.solener.2022.01.004.
Pełny tekst źródłaJia, Xiaowei, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach i 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.
Pełny tekst źródłaHagmeyer, Simon, Peter Zeiler i 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.
Pełny tekst źródłaZeng, Shi, i 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.
Pełny tekst źródłaGurgen, Anil, i Nam T. Dinh. "Development and assessment of a reactor system prognosis model with physics-guided machine learning". Nuclear Engineering and Design 398 (listopad 2022): 111976. http://dx.doi.org/10.1016/j.nucengdes.2022.111976.
Pełny tekst źródłaPiccione, A., J. W. Berkery, S. A. Sabbagh i 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.
Pełny tekst źródłaJurj, Sorin Liviu, Dominik Grundt, Tino Werner, Philipp Borchers, Karina Rothemann i 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.
Pełny tekst źródłaLiang, Yu, i Dalei Wu. "Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction". Journal of Systemics, Cybernetics and Informatics 20, nr 5 (październik 2022): 148–59. http://dx.doi.org/10.54808/jsci.20.05.148.
Pełny tekst źródłaCheng, Baolian, i Paul A. Bradley. "What Machine Learning Can and Cannot Do for Inertial Confinement Fusion". Plasma 6, nr 2 (1.06.2023): 334–44. http://dx.doi.org/10.3390/plasma6020023.
Pełny tekst źródłaQin, Yue, Changyu Su, Dongdong Chu, Jicai Zhang i Jinbao Song. "A Review of Application of Machine Learning in Storm Surge Problems". Journal of Marine Science and Engineering 11, nr 9 (1.09.2023): 1729. http://dx.doi.org/10.3390/jmse11091729.
Pełny tekst źródłaCunha, Barbara Zaparoli, Abdel-Malek Zine, Mohamed Ichchou, Christophe Droz i 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.
Pełny tekst źródłaWu, Xiaoqin, i 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.
Pełny tekst źródłaNguyen, Cong Tien, Selda Oterkus i Erkan Oterkus. "A physics-guided machine learning model for two-dimensional structures based on ordinary state-based peridynamics". Theoretical and Applied Fracture Mechanics 112 (kwiecień 2021): 102872. http://dx.doi.org/10.1016/j.tafmec.2020.102872.
Pełny tekst źródłaWiedemann, Arthur, Christopher Fuller i 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.
Pełny tekst źródłaLiang, Lin, i Ting Lei. "Machine-Learning-Enabled Automatic Sonic Shear Processing". Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 62, nr 3 (1.06.2021): 282–95. http://dx.doi.org/10.30632/pjv62n3-2021a3.
Pełny tekst źródłaGulick, 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.
Pełny tekst źródłaKo, Hyunwoong, Yan Lu, Zhuo Yang, Ndeye Y. Ndiaye i Paul Witherell. "A framework driven by physics-guided machine learning for process-structure-property causal analytics in additive manufacturing". Journal of Manufacturing Systems 67 (kwiecień 2023): 213–28. http://dx.doi.org/10.1016/j.jmsy.2022.09.010.
Pełny tekst źródłaZhang, Junru, Yang Liu, Durga Chandra Sekhar.P, Manjot Singh, Yuxin Tong, Ezgi Kucukdeger, Hu Young Yoon i in. "Rapid, autonomous high-throughput characterization of hydrogel rheological properties via automated sensing and physics-guided machine learning". Applied Materials Today 30 (luty 2023): 101720. http://dx.doi.org/10.1016/j.apmt.2022.101720.
Pełny tekst źródłaCao, Bin, Shuang Yang, Ankang Sun, Ziqiang Dong i 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.
Pełny tekst źródłaRai, Rahul, i 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.
Pełny tekst źródłaDhara, Arnab, i Mrinal K. Sen. "Physics-guided deep autoencoder to overcome the need for a starting model in full-waveform inversion". Leading Edge 41, nr 6 (czerwiec 2022): 375–81. http://dx.doi.org/10.1190/tle41060375.1.
Pełny tekst źródłaFadziso, Takudzwa. "Enhancing Predictions in Ungauged Basins Using Machine Learning to Its Full Potential". Asian Journal of Applied Science and Engineering 8, nr 1 (5.05.2019): 35–50. http://dx.doi.org/10.18034/ajase.v8i1.10.
Pełny tekst źródłaSuryani, Dewi, Mohamad Nur i 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.
Pełny tekst źródłaBiswas, Reetam, Mrinal K. Sen, Vishal Das i Tapan Mukerji. "Prestack and poststack inversion using a physics-guided convolutional neural network". Interpretation 7, nr 3 (1.08.2019): SE161—SE174. http://dx.doi.org/10.1190/int-2018-0236.1.
Pełny tekst źródłaXie, Yazhou, Majid Ebad Sichani, Jamie E. Padgett i Reginald DesRoches. "The promise of implementing machine learning in earthquake engineering: A state-of-the-art review". Earthquake Spectra 36, nr 4 (3.06.2020): 1769–801. http://dx.doi.org/10.1177/8755293020919419.
Pełny tekst źródłaZhang, Hongliang, Kristopher A. Innanen i 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.
Pełny tekst źródłaGopalakrishnan 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 i 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.
Pełny tekst źródłaNaser, 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 (listopad 2021): 110098. http://dx.doi.org/10.1016/j.measurement.2021.110098.
Pełny tekst źródłaSeyyedi, Azra, Mahdi Bohlouli i SeyedEhsan Nedaaee Oskoee. "Machine Learning and Physics: A Survey of Integrated Models". ACM Computing Surveys, 3.08.2023. http://dx.doi.org/10.1145/3611383.
Pełny tekst źródłaGarpelli, Lucas Nogueira, Diogo Stuani Alves, Katia Lucchesi Cavalca i 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.
Pełny tekst źródłaZhao, Luanxiao, Jingyu Liu, Minghui Xu, Zhenyu Zhu, Yuanyuan Chen i 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.
Pełny tekst źródłaBraiek, Houssem Ben, Thomas Reid i 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.
Pełny tekst źródłaChen, Jie, i Yongming Liu. "Probabilistic physics-guided machine learning for fatigue data analysis". Expert Systems with Applications, listopad 2020, 114316. http://dx.doi.org/10.1016/j.eswa.2020.114316.
Pełny tekst źródłaNakazawa, Ryota, Yuki Minamoto, Nakamasa Inoue i Mamoru Tanahashi. "Species reaction rate modelling based on physics-guided machine learning". Combustion and Flame, sierpień 2021, 111696. http://dx.doi.org/10.1016/j.combustflame.2021.111696.
Pełny tekst źródłaGreis, Noel P., Monica L. Nogueira, Sambit Bhattacharya, Catherine Spooner i Tony Schmitz. "Stability modeling for chatter avoidance in self-aware machining: an application of physics-guided machine learning". Journal of Intelligent Manufacturing, 9.11.2022. http://dx.doi.org/10.1007/s10845-022-01999-w.
Pełny tekst źródłaGuo, Lulu, Jin Ye i 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.
Pełny tekst źródłaAbu-Mualla, Mohammad, i Jida Huang. "Inverse Design of 3D Cellular Materials with Physics-Guided Machine Learning". Materials & Design, lipiec 2023, 112103. http://dx.doi.org/10.1016/j.matdes.2023.112103.
Pełny tekst źródłaChen, Shengyu, Nasrin Kalanat, Yiqun Xie, Sheng Li, Jacob A. Zwart, Jeffrey M. Sadler, Alison P. Appling, Samantha K. Oliver, Jordan S. Read i 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.
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