Auswahl der wissenschaftlichen Literatur zum Thema „Physics-guided Machine Learning“
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Zeitschriftenartikel zum Thema "Physics-guided Machine Learning"
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 QuelleDissertationen zum Thema "Physics-guided Machine Learning"
Brandão, Eduardo. „Complexity Methods in Physics-Guided Machine Learning“. Electronic Thesis or Diss., Saint-Etienne, 2023. http://www.theses.fr/2023STET0062.
Der volle Inhalt der QuelleComplexity is easy to recognize but difficult to define: there are a host of measures of complexity, each relevant for a particular application.In Surface engineering, self-organization drives the formation of patterns on matter by femtosecond laser irradiation, which have important biomedical applications. Pattern formation details are not fully understood. In work leading to two publications [1,2], via a complexity argument and a physics-guided machine learning framework, we show that the severely constrained problem of learning the laser-matter interaction with few data and partial physical knowledge is well-posed in this context. Our model allows us to make useful predictions and suggests physical insights.In another contribution [3] we propose a new formulation of the Minimum Description Length principle, defining model and data complexity in a single step, by taking into account signal and noise in training data. Experiments indicate that Neural Network classifiers that generalize well follow this principle.In unpublished work, we propose Taylor entropy, a novel measure of dynamical system complexity which can be estimated via a single SEM image. This approach could facilitate learning the physical process in new materials through domain adaptation.This thesis paves the way for a unified representation of complexity in data and physical knowledge, which can be used in the context of Physics-guided machine learning.[1] Brandao, Eduardo, et al. "Learning PDE to model self-organization of matter." Entropy 24.8 (2022): 1096.[2] Brandao, Eduardo, et al. "Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns." Physical Review Letters 130.22 (2023): 226201.[3] Brandao, Eduardo, et al. "Is My Neural Net Driven by the MDL Principle?." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer Nature Switzerland, 2023
Shahdi, Arya. „Physics-guided Machine Learning Approaches for Applications in Geothermal Energy Prediction“. Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103603.
Der volle Inhalt der QuelleMaster of Science
Machine learning and artificial intelligence have transformed many research fields and industries. In this thesis, we investigate the applicability of machine learning and data-driven approaches in the field of geothermal energy exploration. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models, we show that machine learning can provide viable alternative solutions for geothermal energy mapping. First, we explore a suite of machine learning algorithms such as neural networks (DNN), Ridge regression (R-reg) models, and decision-tree based models (e.g., XGBoost and Random Forest). We find that XGBoost and Random Forests result in the highest accuracy for subsurface temperature prediction. Accuracy measures show that machine learning models are at par with physics-based models and can even outperform the thermal conductivity model. Second, we incorporate the thermal conductivity theory with machine learning and propose an innovative clustering-regression approach in the emerging area of physics-guided machine learning that results in a smaller error than black-box machine learning methods.
Lundström, Robin. „Machine Learning for Air Flow Characterization : An application of Theory-Guided Data Science for Air Fow characterization in an Industrial Foundry“. Thesis, Karlstads universitet, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72782.
Der volle Inhalt der QuelleIndustriarbetare utsätts för skadliga luftburna ämnen vilket över tid leder till högre prevalens för lungsjukdomar så som kronisk obstruktiv lungsjukdom, stendammslunga och lungcancer. De nuvarande luftmätningsmetoderna genomförs årligen under korta sessioner och ofta vid få selekterade platser i industrilokalen. I denna masteruppsats presenteras en teorivägledd datavetenskapsmodell (TGDS) som kombinerar en stationär beräkningsströmningsdynamik (CFD) modell med en dynamisk maskininlärningsmodell. Både CFD-modellen och maskininlärningsalgoritmen utvecklades i Matlab. Echo State Network (ESN) användes för att träna maskininlärningsmodellen och Gaussiska Processer (GP) används som regressionsteknik för att kartlägga luftflödet över hela industrilokalen. Att kombinera ESN med GP för att uppskatta luftflöden i stålverk genomfördes första gången 2016 och denna modell benämns Echo State Map (ESM). Nätverket använder data från fem stationära sensorer och tränades på differensen mellan CFD-modellen och mätningar genomfördes med en mobil robot på olika platser i industriområdet. Maskininlärningsmodellen modellerar således de dynamiska effekterna i industrilokalen som den stationära CFD-modellen inte tar hänsyn till. Den presenterade modellen uppvisar lika hög temporal och rumslig upplösning som echo state map medan den också återger fysikalisk konsistens som CFD-modellen. De initiala applikationerna för denna model påvisar att de främsta egenskaperna hos echo state map och CFD används i symbios för att ge förbättrad karakteriseringsförmåga. Den presenterade modellen kan spela en viktig roll för framtida karakterisering av luftflöden i industrilokaler och fler studier är nödvändiga innan full förståelse av denna model uppnås.
Bücher zum Thema "Physics-guided Machine Learning"
Di Ventra, Massimiliano. MemComputing. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192845320.001.0001.
Der volle Inhalt der QuelleVan Dyk, Jacob. The Modern Technology of Radiation Oncology, Vol 4. Medical Physics Publishing, 2020. http://dx.doi.org/10.54947/9781951134020.
Der volle Inhalt der QuelleBuchteile zum Thema "Physics-guided Machine Learning"
Wang, Rui, Robin Walters und Rose Yu. „Physics-Guided Deep Learning for Spatiotemporal Forecasting“. In Knowledge-Guided Machine Learning, 179–210. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-8.
Der volle Inhalt der QuelleJia, Xiaowei, Jared D. Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach und Vipin Kumar. „Physics-Guided Recurrent Neural Networks for Predicting Lake Water Temperature“. In Knowledge-Guided Machine Learning, 373–98. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-16.
Der volle Inhalt der QuelleDaw, Arka, Anuj Karpatne, William D. Watkins, Jordan S. Read und Vipin Kumar. „Physics-Guided Neural Networks (PGNN): An Application in Lake Temperature Modeling“. In Knowledge-Guided Machine Learning, 353–72. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-15.
Der volle Inhalt der QuelleDaw, Arka, R. Quinn Thomas, Cayelan C. Carey, Jordan S. Read, Alison P. Appling und Anuj Karpatne. „Physics-Guided Architecture (PGA) of LSTM Models for Uncertainty Quantification in Lake Temperature Modeling“. In Knowledge-Guided Machine Learning, 399–416. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-17.
Der volle Inhalt der QuelleBond, Robert Bailey, Pu Ren, Hao Sun und Jerome F. Hajjar. „Physics-Guided Machine Learning for Structural Metamodeling and Fragility Analysis“. In Lecture Notes in Civil Engineering, 855–66. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62884-9_75.
Der volle Inhalt der QuelleHe, Erhu, Yiqun Xie, Licheng Liu, Zhenong Jin, Dajun Zhang und Xiaowei Jia. „Knowledge Guided Machine Learning for Extracting, Preserving, and Adapting Physics-aware Features“. In Proceedings of the 2024 SIAM International Conference on Data Mining (SDM), 715–23. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2024. http://dx.doi.org/10.1137/1.9781611978032.82.
Der volle Inhalt der QuelleMishra, Vaibhav, Sachin Kumar und Mohammed Rabius Sunny. „A Hybrid Physics and Machine Learning Based Approach for Guided Wave Based Detection of Delaminations in FRP Composites“. In Proceedings of the First International Conference on Aeronautical Sciences, Engineering and Technology, 235–42. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7775-8_24.
Der volle Inhalt der QuelleWang, Sifan, und Paris Perdikaris. „Adaptive Training Strategies for Physics-Informed Neural Networks“. In Knowledge-Guided Machine Learning, 133–60. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-6.
Der volle Inhalt der QuelleZhang, Zhibo, Ryan Nguyen, Souma Chowdhury und Rahul Rai. „Physics-Infused Learning: A DNN and GAN Approach“. In Knowledge-Guided Machine Learning, 305–26. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-13.
Der volle Inhalt der QuelleBrunton, Steven L., und J. Nathan Kutz. „Targeted Use of Deep Learning for Physics and Engineering“. In Knowledge-Guided Machine Learning, 31–54. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-2.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Physics-guided Machine Learning"
Klein, Natalie, Adra Carr, Zigfried Hampel-Arias, Amanda Ziemann und Eric Flynn. „Physics-guided neural networks for hyperspectral target identification“. In Applications of Machine Learning 2023, herausgegeben von Barath Narayanan Narayanan, Michael E. Zelinski, Tarek M. Taha und Jonathan Howe. SPIE, 2023. http://dx.doi.org/10.1117/12.2684140.
Der volle Inhalt der QuelleGhosh, Abantika, Mohannad Elhamod, Jie Bu, Wei-Cheng Lee, Anuj Karpatne und Viktor A. Podolskiy. „Physics-guided machine learning for Maxwell's equations“. In Metamaterials, Metadevices, and Metasystems 2021, herausgegeben von Nader Engheta, Mikhail A. Noginov und Nikolay I. Zheludev. SPIE, 2021. http://dx.doi.org/10.1117/12.2594575.
Der volle Inhalt der QuelleGupta, Utkarsh, Anish Gorantiwar und Saied Taheri. „Vehicle Suspension Control using Physics Guided Machine Learning“. In 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2023. http://dx.doi.org/10.1109/hora58378.2023.10156788.
Der volle Inhalt der QuelleRauseo, Marco, Fanzhou Zhao und Mehdi Vahdati. „Physics guided machine learning modelling of compressor aeroelastic flutter“. In GPPS Hong Kong23. GPPS, 2023. http://dx.doi.org/10.33737/gpps23-tc-196.
Der volle Inhalt der QuelleNhu, Anh N., Ngoc-Anh Le, Shihang Li und Thang D. V. Truong. „Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control“. In 2023 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2023. http://dx.doi.org/10.1109/icmla58977.2023.00065.
Der volle Inhalt der QuelleLin, L., N. Dinh und A. Gurgen. „Development and Assessment of Physics-guided Machine Learning for Prognosis System“. In 2020 ANS Virtual Winter Meeting. AMNS, 2020. http://dx.doi.org/10.13182/t123-33503.
Der volle Inhalt der QuelleAl-Younis, Wardeh, Steven Sandoval, David Voelz und Mohammad Abdullah-Al-Mamun. „A Physics-Guided Machine Learning Model for the Prediction of Atmospheric Refraction“. In Propagation Through and Characterization of Atmospheric and Oceanic Phenomena. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/pcaop.2022.pth4f.4.
Der volle Inhalt der QuelleFukui, Ken-ichi, Junya Tanaka, Tomohiko Tomita und Masayuki Numao. „Physics-Guided Neural Network with Model Discrepancy Based on Upper Troposphere Wind Prediction“. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00078.
Der volle Inhalt der QuelleChen, Jie, und Yongming Liu. „Physics-guided machine learning for multi-factor fatigue analysis and uncertainty quantification“. In AIAA Scitech 2021 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2021. http://dx.doi.org/10.2514/6.2021-1242.
Der volle Inhalt der QuelleStaff, Gunnar, Gustavo Zarruk, Johan Hatleskog, Simon Stavland, Henry McNulty, Roberto Ibarra, Nicholas Calen et al. „Physics Guided Machine Learning Significantly Improves Outcomes for Data-Based Production Optimization“. In Abu Dhabi International Petroleum Exhibition & Conference. Society of Petroleum Engineers, 2020. http://dx.doi.org/10.2118/202657-ms.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Physics-guided Machine Learning"
Lin, Youzuo. Physics-guided Machine Learning: from Supervised Deep Networks to Unsupervised Lightweight Models. Office of Scientific and Technical Information (OSTI), August 2023. http://dx.doi.org/10.2172/1994110.
Der volle Inhalt der QuelleSun, Alexander, Bridget Scanlon, Clint Dawson, Paola Passalacqua, Dev Niyogi, Zong-Liang Yang und Susanne Pierce. Bridging Multiscale Processes in Earth System Models with Physics-Guided Hierarchical Machine Learning. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769682.
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