Relevant bibliographies by topics / Physics-guided Machine Learning

Academic literature on the topic 'Physics-guided Machine Learning'

Author: Grafiati
Published: 7 July 2024
Last updated: 7 July 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Physics-guided Machine Learning":

1

Pawar, Suraj, Omer San, Burak Aksoylu, Adil Rasheed, and Trond Kvamsdal. "Physics guided machine learning using simplified theories." Physics of Fluids 33, no. 1 (January 1, 2021): 011701. http://dx.doi.org/10.1063/5.0038929.

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Pawar, Suraj, Omer San, Burak Aksoylu, Adil Rasheed, and Trond Kvamsdal. "Physics guided machine learning using simplified theories." Physics of Fluids 33, no. 1 (January 1, 2021): 011701. http://dx.doi.org/10.1063/5.0038929.

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Jørgensen, 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.

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Abstract This paper presents a method for energy efficient weather routing of a ferry in Norway. Historical operational data from the ferry and environmental data are used to develop two models that predict the energy consumption. The first is a purely data-driven linear regression energy model, while the second is as a hybrid model, combining physical models with data-driven models using machine learning techniques. With an established energy model, it is possible to develop a route optimization that proposes efficient routes with less energy usage compared to fixed speed and heading control.
4

Winter, B., J. Schilling, and A. Bardow. "Physics‐guided machine learning to predict activity coefficients from SMILES." Chemie Ingenieur Technik 94, no. 9 (August 25, 2022): 1320. http://dx.doi.org/10.1002/cite.202255153.

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Ahmed, Shady E., Omer San, Adil Rasheed, Traian Iliescu, and Alessandro Veneziani. "Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling." SIAM Journal on Scientific Computing 45, no. 3 (June 6, 2023): B283—B313. http://dx.doi.org/10.1137/22m1496360.

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Banyay, Gregory A., and Andrew S. Wixom. "Predictive capability assessment for physics-guided learning of vortex-induced vibrations." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A48. http://dx.doi.org/10.1121/10.0015496.

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We seek here a computationally parsimonious and credible means to simulate the complex phenomena of vortex-induced vibrations (ViV), as one tool to assist in mitigating risk associated with ViV-induced instabilities that can cause non-negligible structural acoustic response. To address current limitations in data-driven modeling, for which credibility assessment proves challenging, or physics-based simulation (i.e., constrained by governing partial differential equations (PDEs)), which often includes prohibitive computational expense, we explore recent state-of-the-art approaches to optimally combine these engineering disciplines via a physics-guided machine learning framework. One can expect that intersecting data-driven modeling with physics-guided simulation offers one means to both maximize the credibility of machine learning based approaches and minimize the computational expense of physics-based modeling approaches.
7

Jia, Xiaowei. "Physics-guided machine learning: A new paradigm for scientific knowledge discovery." Microscopy and Microanalysis 27, S1 (July 30, 2021): 1344–45. http://dx.doi.org/10.1017/s1431927621005018.

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Yu, Yang, Houpu Yao, and 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.

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Pawar, Suraj, Omer San, Aditya Nair, Adil Rasheed, and Trond Kvamsdal. "Model fusion with physics-guided machine learning: Projection-based reduced-order modeling." Physics of Fluids 33, no. 6 (June 2021): 067123. http://dx.doi.org/10.1063/5.0053349.

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Hoerig, Cameron, Jamshid Ghaboussi, and Michael F. Insana. "Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging." Physics in Medicine & Biology 65, no. 6 (March 20, 2020): 065011. http://dx.doi.org/10.1088/1361-6560/ab7505.

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Journal articles

Dissertations / Theses on the topic "Physics-guided Machine Learning":

1

Brandão, Eduardo. "Complexity Methods in Physics-Guided Machine Learning." Electronic Thesis or Diss., Saint-Etienne, 2023. http://www.theses.fr/2023STET0062.

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La complexité est facile à reconnaître mais difficile à définir : il existe de nombreuses mesures de complexité, chacune pertinente pour une application particulière.Dans le domaine de l'ingénierie des surfaces, l'auto-organisation entraîne la formation de motifs sur la matière par irradiation laser femtoseconde, ce qui a d'importantes applications biomédicales. Les détails de la formation des motifs ne sont pas entièrement compris. Dans des travaux menant à deux publications [1,2], grâce à un argument de complexité et un cadre d'apprentissage automatique guidé par la physique, nous montrons que le problème sévèrement contraint d'apprendre l'interaction laser-matière avec peu de données et une connaissance physique partielle est bien posé dans ce contexte. Notre modèle nous permet de faire des prédictions utiles et suggère des intuitions physiques.Dans une autre contribution [3], nous proposons une nouvelle formulation du principe de la Longueur Minimale de Description, définissant la complexité du modèle et des données en une seule étape, en tenant compte du signal et du bruit dans les données d'entraînement. Les expériences indiquent que les classificateurs de réseaux neuronaux qui généralisent bien suivent ce principe.Dans un travail non publié, nous proposons l'entropie de Taylor, une nouvelle mesure de la complexité des systèmes dynamiques qui peut être estimée via une seule image SEM. Cette approche pourrait faciliter l'apprentissage du processus physique dans de nouveaux matériaux grâce à l'adaptation de domaine.Cette thèse ouvre la voie à une représentation unifiée de la complexité dans les données et la connaissance physique, qui peut être utilisée dans le contexte de l'apprentissage automatique guidé par la physique.[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
Complexity 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
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Shahdi, Arya. "Physics-guided Machine Learning Approaches for Applications in Geothermal Energy Prediction." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103603.

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In the area of geothermal energy mapping, scientists have used physics-based models and bottom-hole temperature measurements from oil and gas wells to generate heat flow and temperature-at-depth maps. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models used in this field, this thesis explores an alternate approach for locating geothermally active regions using machine learning methods coupled with physics knowledge of geothermal energy problems, in the emerging field of physics-guided machine learning. There are two primary contributions of this thesis. First, we present a thorough analysis of using state-of-the-art machine learning models to predict a subsurface geothermal parameter, temperature-at-depth, using a rich geo-spatial dataset across the Appalachian Basin. Specifically, 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 found that XGBoost and Random Forests result in the highest accuracy for subsurface temperature prediction. We also ran our model on a fine spatial grid to provide 2D continuous temperature maps at three different depths using the XGBoost model, which can be used to locate prospective geothermally active regions. Second, we develop a physics-guided machine learning model for predicting subsurface temperatures that not only uses surface temperature, thermal conductivity coefficient, and depth as input parameters, but also the heat-flux parameter that is known to be a potent indicator of temperature-at-depth values according to physics knowledge of geothermal energy problems. Since, there is no independent easy-to-use method for observing heat-flux directly or inferring it from other observed variables. We develop an innovative approach to take into account heat-flux parameters through a physics-guided clustering-regression model. Specifically, the bottom-hole temperature data is initially clustered into multiple groups based on the heat-flux parameter using Gaussian mixture model (GMM). This is followed by training neural network regression models using the data within each constant heat-flux region. Finally, a KNN classifier is trained for cluster membership prediction. Our preliminary results indicate that our proposed approach results in lower errors as the number of clusters increases because the heat-flux parameter is indirectly accounted for in the machine learning model.
Master 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.
3

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.

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In industrial environments, operators are exposed to polluted air which after constant exposure can cause irreversible lethal diseases such as lung cancer. The current air monitoring techniques are carried out sparely in either a single day annually or at few measurement positions for a few days.In this thesis a theory-guided data science (TGDS) model is presented. This hybrid model combines a steady state Computational Fluid Dynamics (CFD) model with a machine learning model. Both the CFD model and the machine learning algorithm was developed in Matlab. The CFD model serves as a basis for the airflow whereas the machine learning model addresses dynamical features in the foundry. Measurements have previously been made at a foundry where five stationary sensors and one mobile robot were used for data acquisition. An Echo State Network was used as a supervised learning technique for airflow predictions at each robot measurement position and Gaussian Processes (GP) were used as a regression technique to form an Echo State Map (ESM). The stationary sensor data were used as input for the echo state network and the difference between the CFD and robot measurements were used as teacher signal which formed a dynamic correction map that was added to the steady state CFD. The proposed model utilizes the high spatio-temporal resolution of the echo state map whilst making use of the physical consistency of the CFD. The initial applications of the novel hybrid model proves that the best qualities of these two models could come together in symbiosis to give enhanced characterizations.The proposed model could have an important role for future characterization of airflow and more research on this and similar topics are encouraged to make sure we properly understand the potential of this novel model.
Industriarbetare 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.

Books on the topic "Physics-guided Machine Learning":

1

Di Ventra, Massimiliano. MemComputing. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192845320.001.0001.

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From the originator of MemComputing comes the very first book on this new computing paradigm that employs time non-locality (memory) to both process and store information. The book discusses the rationale behind MemComputing, its theoretical foundations, and wide-range applicability to combinatorial optimization problems, Machine Learning, and Quantum Mechanics. The book is ideal for graduate students in Physics, Computer Science, Electrical Engineering, and Mathematics as well as researchers in both academia and industry interested in unconventional computing. The author relies on extensive margin notes, important remarks, and several artworks to better explain the main concepts and clarify all the jargon, making the book as self-contained as possible. The reader will be guided from the basic notions to the more advanced ones with a writing style that is always clear and engaging. Along the way, the reader will appreciate the advantages of this computing paradigm and the major differences that set it apart from the prevailing Turing model of computation, and even Quantum Computing.
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Van Dyk, Jacob. The Modern Technology of Radiation Oncology, Vol 4. Medical Physics Publishing, 2020. http://dx.doi.org/10.54947/9781951134020.

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High praise continues to come in for the 4th volume of Jake Van Dyk's The Modern Technology of Radiation Oncology. From Peter Metcalfe in Physical and Engineering Sciences in Medicine… "Thank goodness medical physics has Jacob Van Dyk. Like Tiger Woods and Phil Mickelson in golf, his textbooks continue to make major comebacks. He has managed to assemble the most talented among us to sustain the up-to-date knowledge that is essential to our profession. Reference knowledge from this textbook will help ensure the medical physics profession is at the cutting edge of cancer research and clinical treatment. This textbook has taken pride of place on my bookshelf, right next to my most treasured Porsche magazines. I could not give it a higher accolade than that." From Rajesh A. Kinhikar in Journal of Medical Physics…"This resourceful book has aimed to serve as a comprehensive textbook for the practicing radiotherapy professionals. I would like to congratulate the authors and the Editor for such a high?quality scientific feast and strongly recommend the fourth volume of The Modern Technology of Radiation Oncologyto the clinical medical physicists and radiation oncology professionals involved with the rapidly evolving radiotherapy." New topics addressed in volume 4 include surface-guided radiation therapy (RT), PET/MRI, real-time MRI guidance, robust optimization, automated treatment planning, artificial intelligence, adaptive RT, machine learning, big data, radiomics, particle therapy RBE, nanoparticle applications, economic considerations, global medical physics activities, global access to RT, and FLASH RT. The volumes in this series have not only been valued by medical physicists and radiation oncologists in clinical practice around the world, but have also provided an important learning resource for residency programs, radiation technologists, dosimetrists, research students, biomedical engineers, and ancillary professionals related with radiotherapy. Administrators and scientists affiliated with the practice of radiation therapy will also find this book a useful resource.

Book chapters on the topic "Physics-guided Machine Learning":

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Wang, Rui, Robin Walters, and 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.

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Jia, Xiaowei, Jared D. Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach, and 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.

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Daw, Arka, Anuj Karpatne, William D. Watkins, Jordan S. Read, and 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.

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Daw, Arka, R. Quinn Thomas, Cayelan C. Carey, Jordan S. Read, Alison P. Appling, and 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.

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Bond, Robert Bailey, Pu Ren, Hao Sun, and 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.

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He, Erhu, Yiqun Xie, Licheng Liu, Zhenong Jin, Dajun Zhang, and 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.

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Mishra, Vaibhav, Sachin Kumar, and 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.

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Wang, Sifan, and 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.

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Zhang, Zhibo, Ryan Nguyen, Souma Chowdhury, and 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.

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Brunton, Steven L., and 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.

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Conference papers on the topic "Physics-guided Machine Learning":

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Klein, Natalie, Adra Carr, Zigfried Hampel-Arias, Amanda Ziemann, and Eric Flynn. "Physics-guided neural networks for hyperspectral target identification." In Applications of Machine Learning 2023, edited by Barath Narayanan Narayanan, Michael E. Zelinski, Tarek M. Taha, and Jonathan Howe. SPIE, 2023. http://dx.doi.org/10.1117/12.2684140.

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Ghosh, Abantika, Mohannad Elhamod, Jie Bu, Wei-Cheng Lee, Anuj Karpatne, and Viktor A. Podolskiy. "Physics-guided machine learning for Maxwell's equations." In Metamaterials, Metadevices, and Metasystems 2021, edited by Nader Engheta, Mikhail A. Noginov, and Nikolay I. Zheludev. SPIE, 2021. http://dx.doi.org/10.1117/12.2594575.

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Gupta, Utkarsh, Anish Gorantiwar, and 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.

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Rauseo, Marco, Fanzhou Zhao, and 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.

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Modern aircraft engines need to meet ever more stringent requirements that greatly increase the complexity of design, which strives for enhanced performance, reduced operating costs, emissions and noise simultaneously. The drive for performance leads to the development of thin, lightweight, highly loaded fan and compressor blades which are increasingly more prone to incur high, sustained vibratory stresses and aeroelastic problems such as flutter. The current practice employs preliminary design tools for flutter that are often based on empiricism or simplified analytical models, requiring extensive use of computational fluid dynamics to verify aeroelastic stability. As the industry moves to new designs, fast and accurate prediction tools are needed. In this work, data-driven techniques are employed to model the aeroelastic response of compressor blades. Machine learning has been applied to a plethora of engineering problems, with particular success in the field of turbulence modelling. However, conventional, black-box data-driven methods based on simple input parameters require large databases and are unable to generalise. In this work a combination of machine learning techniques and reduced order models is proposed to address both limitations at the same time. Previous knowledge of flutter is introduced in the physics guided framework by formulating relevant, steady state input features, and by injecting results from low-fidelity analytical models. The models are tested on several unseen cascades and it is found that training on even a single geometry yields accurate results. The models developed here allow flutter prediction of fan and compressor flutter stability based on the steady state flow only without a need for any CPU intensive unsteady simulations. Hence, one can predict flutter stability of a given blade for different mechanical properties (mode shape, frequency) at near zero additional cost once the mean flow is known.
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Nhu, Anh N., Ngoc-Anh Le, Shihang Li, and 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.

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Lin, L., N. Dinh, and 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.

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Al-Younis, Wardeh, Steven Sandoval, David Voelz, and 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.

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Image feature shifts due to atmospheric refraction are predicted with a finite difference machine learning method based on physics-infused modeling and data-driven training using time-lapse imagery. The model’s performance is compared with a previous approach.
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Fukui, Ken-ichi, Junya Tanaka, Tomohiko Tomita, and 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.

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Chen, Jie, and 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.

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Staff, 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.

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Reports on the topic "Physics-guided Machine Learning":

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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.

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Sun, Alexander, Bridget Scanlon, Clint Dawson, Paola Passalacqua, Dev Niyogi, Zong-Liang Yang, and 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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