Artigos de revistas sobre o tema "Physics-guided Machine Learning"
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Pawar, Suraj, Omer San, Burak Aksoylu, Adil Rasheed e Trond Kvamsdal. "Physics guided machine learning using simplified theories". Physics of Fluids 33, n.º 1 (1 de janeiro de 2021): 011701. http://dx.doi.org/10.1063/5.0038929.
Texto completo da fontePawar, Suraj, Omer San, Burak Aksoylu, Adil Rasheed e Trond Kvamsdal. "Physics guided machine learning using simplified theories". Physics of Fluids 33, n.º 1 (1 de janeiro de 2021): 011701. http://dx.doi.org/10.1063/5.0038929.
Texto completo da fonteJørgensen, Ulrik, Pauline Røstum Belingmo, Brian Murray, Svein Peder Berge e Armin Pobitzer. "Ship route optimization using hybrid physics-guided machine learning". Journal of Physics: Conference Series 2311, n.º 1 (1 de julho de 2022): 012037. http://dx.doi.org/10.1088/1742-6596/2311/1/012037.
Texto completo da fonteWinter, B., J. Schilling e A. Bardow. "Physics‐guided machine learning to predict activity coefficients from SMILES". Chemie Ingenieur Technik 94, n.º 9 (25 de agosto de 2022): 1320. http://dx.doi.org/10.1002/cite.202255153.
Texto completo da fonteAhmed, Shady E., Omer San, Adil Rasheed, Traian Iliescu e Alessandro Veneziani. "Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling". SIAM Journal on Scientific Computing 45, n.º 3 (6 de junho de 2023): B283—B313. http://dx.doi.org/10.1137/22m1496360.
Texto completo da fonteBanyay, Gregory A., e Andrew S. Wixom. "Predictive capability assessment for physics-guided learning of vortex-induced vibrations". Journal of the Acoustical Society of America 152, n.º 4 (outubro de 2022): A48. http://dx.doi.org/10.1121/10.0015496.
Texto completo da fonteJia, Xiaowei. "Physics-guided machine learning: A new paradigm for scientific knowledge discovery". Microscopy and Microanalysis 27, S1 (30 de julho de 2021): 1344–45. http://dx.doi.org/10.1017/s1431927621005018.
Texto completo da fonteYu, Yang, Houpu Yao e Yongming Liu. "Structural dynamics simulation using a novel physics-guided machine learning method". Engineering Applications of Artificial Intelligence 96 (novembro de 2020): 103947. http://dx.doi.org/10.1016/j.engappai.2020.103947.
Texto completo da fontePawar, Suraj, Omer San, Aditya Nair, Adil Rasheed e Trond Kvamsdal. "Model fusion with physics-guided machine learning: Projection-based reduced-order modeling". Physics of Fluids 33, n.º 6 (junho de 2021): 067123. http://dx.doi.org/10.1063/5.0053349.
Texto completo da fonteHoerig, Cameron, Jamshid Ghaboussi e Michael F. Insana. "Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging". Physics in Medicine & Biology 65, n.º 6 (20 de março de 2020): 065011. http://dx.doi.org/10.1088/1361-6560/ab7505.
Texto completo da fonteTetali, Harsha Vardhan, e Joel Harley. "A physics-informed machine learning based dispersion curve estimation for non-homogeneous media". Journal of the Acoustical Society of America 152, n.º 4 (outubro de 2022): A239. http://dx.doi.org/10.1121/10.0016136.
Texto completo da fonteTeurtrie, Adrien, Nathanaël Perraudin, Thomas Holvoet, Hui Chen, Duncan T. L. Alexander, Guillaume Obozinski e Cécile Hébert. "Physics-Guided Machine Learning for the Analysis of Low SNR STEM-EDXS Data". Microscopy and Microanalysis 28, S1 (22 de julho de 2022): 2978–79. http://dx.doi.org/10.1017/s1431927622011163.
Texto completo da fonteBuster, Grant, Mike Bannister, Aron Habte, Dylan Hettinger, Galen Maclaurin, Michael Rossol, Manajit Sengupta e Yu Xie. "Physics-guided machine learning for improved accuracy of the National Solar Radiation Database". Solar Energy 232 (janeiro de 2022): 483–92. http://dx.doi.org/10.1016/j.solener.2022.01.004.
Texto completo da fonteJia, Xiaowei, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach e Vipin Kumar. "Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles". ACM/IMS Transactions on Data Science 2, n.º 3 (17 de maio de 2021): 1–26. http://dx.doi.org/10.1145/3447814.
Texto completo da fonteHagmeyer, Simon, Peter Zeiler e 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, n.º 1 (29 de junho de 2022): 156–65. http://dx.doi.org/10.36001/phme.2022.v7i1.3352.
Texto completo da fonteZeng, Shi, e Dechang Pi. "Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning". Sensors 23, n.º 10 (22 de maio de 2023): 4969. http://dx.doi.org/10.3390/s23104969.
Texto completo da fonteGurgen, Anil, e Nam T. Dinh. "Development and assessment of a reactor system prognosis model with physics-guided machine learning". Nuclear Engineering and Design 398 (novembro de 2022): 111976. http://dx.doi.org/10.1016/j.nucengdes.2022.111976.
Texto completo da fontePiccione, A., J. W. Berkery, S. A. Sabbagh e Y. Andreopoulos. "Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas". Nuclear Fusion 60, n.º 4 (18 de março de 2020): 046033. http://dx.doi.org/10.1088/1741-4326/ab7597.
Texto completo da fonteJurj, Sorin Liviu, Dominik Grundt, Tino Werner, Philipp Borchers, Karina Rothemann e Eike Möhlmann. "Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning". Energies 14, n.º 22 (12 de novembro de 2021): 7572. http://dx.doi.org/10.3390/en14227572.
Texto completo da fonteLiang, Yu, e Dalei Wu. "Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction". Journal of Systemics, Cybernetics and Informatics 20, n.º 5 (outubro de 2022): 148–59. http://dx.doi.org/10.54808/jsci.20.05.148.
Texto completo da fonteCheng, Baolian, e Paul A. Bradley. "What Machine Learning Can and Cannot Do for Inertial Confinement Fusion". Plasma 6, n.º 2 (1 de junho de 2023): 334–44. http://dx.doi.org/10.3390/plasma6020023.
Texto completo da fonteQin, Yue, Changyu Su, Dongdong Chu, Jicai Zhang e Jinbao Song. "A Review of Application of Machine Learning in Storm Surge Problems". Journal of Marine Science and Engineering 11, n.º 9 (1 de setembro de 2023): 1729. http://dx.doi.org/10.3390/jmse11091729.
Texto completo da fonteCunha, Barbara Zaparoli, Abdel-Malek Zine, Mohamed Ichchou, Christophe Droz e Stéphane Foulard. "On Machine-Learning-Driven Surrogates for Sound Transmission Loss Simulations". Applied Sciences 12, n.º 21 (23 de outubro de 2022): 10727. http://dx.doi.org/10.3390/app122110727.
Texto completo da fonteWu, Xiaoqin, e Yipei Wang. "A physics-based machine learning approach for modeling the complex reflection coefficients of metal nanowires". Nanotechnology 33, n.º 20 (21 de fevereiro de 2022): 205701. http://dx.doi.org/10.1088/1361-6528/ac512e.
Texto completo da fonteNguyen, Cong Tien, Selda Oterkus e Erkan Oterkus. "A physics-guided machine learning model for two-dimensional structures based on ordinary state-based peridynamics". Theoretical and Applied Fracture Mechanics 112 (abril de 2021): 102872. http://dx.doi.org/10.1016/j.tafmec.2020.102872.
Texto completo da fonteWiedemann, Arthur, Christopher Fuller e 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, n.º 1 (24 de junho de 2022): 151–62. http://dx.doi.org/10.3397/nc-2022-709.
Texto completo da fonteLiang, Lin, e Ting Lei. "Machine-Learning-Enabled Automatic Sonic Shear Processing". Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 62, n.º 3 (1 de junho de 2021): 282–95. http://dx.doi.org/10.30632/pjv62n3-2021a3.
Texto completo da fonteGulick, Walter. "“Rules of Rightness” and the Evolutionary Emergence of Purpose". Tradition and Discovery: The Polanyi Society Periodical 49, n.º 1 (2023): 21–26. http://dx.doi.org/10.5840/traddisc20234914.
Texto completo da fonteKo, Hyunwoong, Yan Lu, Zhuo Yang, Ndeye Y. Ndiaye e Paul Witherell. "A framework driven by physics-guided machine learning for process-structure-property causal analytics in additive manufacturing". Journal of Manufacturing Systems 67 (abril de 2023): 213–28. http://dx.doi.org/10.1016/j.jmsy.2022.09.010.
Texto completo da fonteZhang, 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 (fevereiro de 2023): 101720. http://dx.doi.org/10.1016/j.apmt.2022.101720.
Texto completo da fonteCao, Bin, Shuang Yang, Ankang Sun, Ziqiang Dong e 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, n.º 2 (2022): 4. http://dx.doi.org/10.20517/jmi.2022.04.
Texto completo da fonteRai, Rahul, e 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.
Texto completo da fonteDhara, Arnab, e Mrinal K. Sen. "Physics-guided deep autoencoder to overcome the need for a starting model in full-waveform inversion". Leading Edge 41, n.º 6 (junho de 2022): 375–81. http://dx.doi.org/10.1190/tle41060375.1.
Texto completo da fonteFadziso, Takudzwa. "Enhancing Predictions in Ungauged Basins Using Machine Learning to Its Full Potential". Asian Journal of Applied Science and Engineering 8, n.º 1 (5 de maio de 2019): 35–50. http://dx.doi.org/10.18034/ajase.v8i1.10.
Texto completo da fonteSuryani, Dewi, Mohamad Nur e Wasis Wasis. "PENGEMBANGAN PROTOTIPE PERANGKAT PEMBELAJARAN FISIKA SMK MODEL INKUIRI TERBIMBING MATERI CERMIN UNTUK MELATIHKAN KETERAMPILAN BERPIKIR KRITIS". JPPS (Jurnal Penelitian Pendidikan Sains) 6, n.º 1 (31 de janeiro de 2017): 1175. http://dx.doi.org/10.26740/jpps.v6n1.p1175-1183.
Texto completo da fonteBiswas, Reetam, Mrinal K. Sen, Vishal Das e Tapan Mukerji. "Prestack and poststack inversion using a physics-guided convolutional neural network". Interpretation 7, n.º 3 (1 de agosto de 2019): SE161—SE174. http://dx.doi.org/10.1190/int-2018-0236.1.
Texto completo da fonteXie, Yazhou, Majid Ebad Sichani, Jamie E. Padgett e Reginald DesRoches. "The promise of implementing machine learning in earthquake engineering: A state-of-the-art review". Earthquake Spectra 36, n.º 4 (3 de junho de 2020): 1769–801. http://dx.doi.org/10.1177/8755293020919419.
Texto completo da fonteZhang, Hongliang, Kristopher A. Innanen e David W. Eaton. "Inversion for Shear-Tensile Focal Mechanisms Using an Unsupervised Physics-Guided Neural Network". Seismological Research Letters 92, n.º 4 (24 de março de 2021): 2282–94. http://dx.doi.org/10.1785/0220200420.
Texto completo da fonteGopalakrishnan 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 e 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, n.º 11 (16 de janeiro de 2023): 228–1. http://dx.doi.org/10.2352/ei.2023.35.11.hpci-228.
Texto completo da fonteNaser, 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 (novembro de 2021): 110098. http://dx.doi.org/10.1016/j.measurement.2021.110098.
Texto completo da fonteSeyyedi, Azra, Mahdi Bohlouli e SeyedEhsan Nedaaee Oskoee. "Machine Learning and Physics: A Survey of Integrated Models". ACM Computing Surveys, 3 de agosto de 2023. http://dx.doi.org/10.1145/3611383.
Texto completo da fonteGarpelli, Lucas Nogueira, Diogo Stuani Alves, Katia Lucchesi Cavalca e Helio Fiori de Castro. "Physics-guided neural networks applied in rotor unbalance problems". Structural Health Monitoring, 10 de abril de 2023, 147592172311630. http://dx.doi.org/10.1177/14759217231163081.
Texto completo da fonteZhao, Luanxiao, Jingyu Liu, Minghui Xu, Zhenyu Zhu, Yuanyuan Chen e Jianhua Geng. "Rock Physics guided machine learning for shear sonic log prediction". GEOPHYSICS, 11 de outubro de 2023, 1–71. http://dx.doi.org/10.1190/geo2023-0152.1.
Texto completo da fonteBraiek, Houssem Ben, Thomas Reid e 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.
Texto completo da fonteChen, Jie, e Yongming Liu. "Probabilistic physics-guided machine learning for fatigue data analysis". Expert Systems with Applications, novembro de 2020, 114316. http://dx.doi.org/10.1016/j.eswa.2020.114316.
Texto completo da fonteNakazawa, Ryota, Yuki Minamoto, Nakamasa Inoue e Mamoru Tanahashi. "Species reaction rate modelling based on physics-guided machine learning". Combustion and Flame, agosto de 2021, 111696. http://dx.doi.org/10.1016/j.combustflame.2021.111696.
Texto completo da fonteGreis, Noel P., Monica L. Nogueira, Sambit Bhattacharya, Catherine Spooner e Tony Schmitz. "Stability modeling for chatter avoidance in self-aware machining: an application of physics-guided machine learning". Journal of Intelligent Manufacturing, 9 de novembro de 2022. http://dx.doi.org/10.1007/s10845-022-01999-w.
Texto completo da fonteGuo, Lulu, Jin Ye e 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.
Texto completo da fonteAbu-Mualla, Mohammad, e Jida Huang. "Inverse Design of 3D Cellular Materials with Physics-Guided Machine Learning". Materials & Design, julho de 2023, 112103. http://dx.doi.org/10.1016/j.matdes.2023.112103.
Texto completo da fonteChen, Shengyu, Nasrin Kalanat, Yiqun Xie, Sheng Li, Jacob A. Zwart, Jeffrey M. Sadler, Alison P. Appling, Samantha K. Oliver, Jordan S. Read e Xiaowei Jia. "Physics-guided machine learning from simulated data with different physical parameters". Knowledge and Information Systems, 31 de março de 2023. http://dx.doi.org/10.1007/s10115-023-01864-z.
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