Готові списки джерел за темами / Physics-informed Machine Learning

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Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Physics-informed Machine Learning"

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Pateras, Joseph, Pratip Rana, and Preetam Ghosh. "A Taxonomic Survey of Physics-Informed Machine Learning." Applied Sciences 13, no. 12 (June 7, 2023): 6892. http://dx.doi.org/10.3390/app13126892.

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Анотація:
Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems lacking sufficient quantity and veracity of data with learning models informed by physically relevant prior information. This work discusses the recent critical advancements in the PIML domain. Novel methods and applications of domain decomposition in physics-informed neural networks (PINNs) in particular are highlighted. Additionally, we explore recent works toward utilizing neural operator learning to intuit relationships in physics systems traditionally modeled by sets of complex governing equations and solved with expensive differentiation techniques. Finally, expansive applications of traditional physics-informed machine learning and potential limitations are discussed. In addition to summarizing recent work, we propose a novel taxonomic structure to catalog physics-informed machine learning based on how the physics-information is derived and injected into the machine learning process. The taxonomy assumes the explicit objectives of facilitating interdisciplinary collaboration in methodology, thereby promoting a wider characterization of what types of physics problems are served by the physics-informed learning machines and assisting in identifying suitable targets for future work. To summarize, the major twofold goal of this work is to summarize recent advancements and introduce a taxonomic catalog for applications of physics-informed machine learning.
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2

Xypakis, Emmanouil, Valeria deTurris, Fabrizio Gala, Giancarlo Ruocco, and Marco Leonetti. "Physics-informed machine learning for microscopy." EPJ Web of Conferences 266 (2022): 04007. http://dx.doi.org/10.1051/epjconf/202226604007.

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Анотація:
We developed a physics-informed deep neural network architecture able to achieve signal to noise ratio improvements starting from low exposure noisy data. Our model is based on the nature of the photon detection process characterized by a Poisson probability distribution which we included in the training loss function. Our approach surpasses previous algorithms performance for microscopy data, moreover, the generality of the physical concepts employed here, makes it readily exportable to any imaging context.
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3

Karimpouli, Sadegh, and Pejman Tahmasebi. "Physics informed machine learning: Seismic wave equation." Geoscience Frontiers 11, no. 6 (November 2020): 1993–2001. http://dx.doi.org/10.1016/j.gsf.2020.07.007.

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4

Barmparis, G. D., and G. P. Tsironis. "Discovering nonlinear resonances through physics-informed machine learning." Journal of the Optical Society of America B 38, no. 9 (August 2, 2021): C120. http://dx.doi.org/10.1364/josab.430206.

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5

Pilania, G., K. J. McClellan, C. R. Stanek, and B. P. Uberuaga. "Physics-informed machine learning for inorganic scintillator discovery." Journal of Chemical Physics 148, no. 24 (June 28, 2018): 241729. http://dx.doi.org/10.1063/1.5025819.

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6

Lagomarsino-Oneto, Daniele, Giacomo Meanti, Nicolò Pagliana, Alessandro Verri, Andrea Mazzino, Lorenzo Rosasco, and Agnese Seminara. "Physics informed machine learning for wind speed prediction." Energy 268 (April 2023): 126628. http://dx.doi.org/10.1016/j.energy.2023.126628.

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7

Tóth, Máté, Adam Brown, Elizabeth Cross, Timothy Rogers, and Neil D. Sims. "Resource-efficient machining through physics-informed machine learning." Procedia CIRP 117 (2023): 347–52. http://dx.doi.org/10.1016/j.procir.2023.03.059.

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Lympany, Shane V., Matthew F. Calton, Mylan R. Cook, Kent L. Gee, and Mark K. Transtrum. "Mapping ambient sound levels using physics-informed machine learning." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A48—A49. http://dx.doi.org/10.1121/10.0015498.

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Анотація:
Mapping the spatial and temporal distribution of ambient sound levels is critical for understanding the impacts of natural sounds and noise pollution on humans and the environment. Previously, ambient sound levels have been predicted using either machine learning or physics-based modeling. Machine learning models have been trained on acoustical measurements at geospatially diverse locations to predict ambient sound levels across the world based on geospatial features. However, machine learning requires a large number of acoustical measurements to predict ambient sound levels at high spatial and temporal resolution. Physics-based models have been applied to predict transportation noise at high spatial and temporal resolution on regional scales, but these predictions do not include other anthropogenic, biological, or geophysical sound sources. In this work, physics-based predictions of transportation noise are combined with machine learning models to predict ambient sound levels at high spatial and temporal resolution across the conterminous United States. The physics-based predictions of transportation noise are incorporated into the machine learning models as a geospatial feature. The result is a physics-informed machine learning model that predicts ambient sound levels at high spatial and temporal resolution across the United States. [Work funded by an Army SBIR]
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Lee, Jonghwan. "Physics-informed machine learning model for bias temperature instability." AIP Advances 11, no. 2 (February 1, 2021): 025111. http://dx.doi.org/10.1063/5.0040100.

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Mondal, B., T. Mukherjee, and T. DebRoy. "Crack free metal printing using physics informed machine learning." Acta Materialia 226 (March 2022): 117612. http://dx.doi.org/10.1016/j.actamat.2021.117612.

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Дисертації з теми "Physics-informed Machine Learning"

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Mack, Jonas. "Physics Informed Machine Learning of Nonlinear Partial Differential Equations." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-441275.

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Wu, Jinlong. "Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85129.

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Анотація:
Reynolds-Averaged Navier-Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or curvature. This lack of accuracy in complex flows has diminished the utility of RANS simulations as a predictive tool for engineering design, analysis, optimization, and reliability assessments. Recently, data-driven methods have emerged as a promising alternative to develop the model of Reynolds stress for RANS simulations. In this dissertation I explore two physics-informed, data-driven frameworks to improve RANS modeled Reynolds stresses. First, a Bayesian inference framework is proposed to quantify and reduce the model-form uncertainty of RANS modeled Reynolds stress by leveraging online sparse measurement data with empirical prior knowledge. Second, a machine-learning-assisted framework is proposed to utilize offline high-fidelity simulation databases. Numerical results show that the data-driven RANS models have better prediction of Reynolds stress and other quantities of interest for several canonical flows. Two metrics are also presented for an a priori assessment of the prediction confidence for the machine-learning-assisted RANS model. The proposed data-driven methods are also applicable to the computational study of other physical systems whose governing equations have some unresolved physics to be modeled.
Ph. D.
Reynolds-Averaged Navier–Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or curvature. This lack of accuracy in complex flows has diminished the utility of RANS simulations as a predictive tool for engineering design, analysis, optimization, and reliability assessments. Recently, data-driven methods have emerged as a promising alternative to develop the model of Reynolds stress for RANS simulations. In this dissertation I explore two physics-informed, data-driven frameworks to improve RANS modeled Reynolds stresses. First, a Bayesian inference framework is proposed to quantify and reduce the model-form uncertainty of RANS modeled Reynolds stress by leveraging online sparse measurement data with empirical prior knowledge. Second, a machine-learning-assisted framework is proposed to utilize offline high fidelity simulation databases. Numerical results show that the data-driven RANS models have better prediction of Reynolds stress and other quantities of interest for several canonical flows. Two metrics are also presented for an a priori assessment of the prediction confidence for the machine-learning-assisted RANS model. The proposed data-driven methods are also applicable to the computational study of other physical systems whose governing equations have some unresolved physics to be modeled.
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Cedergren, Linnéa. "Physics-informed Neural Networks for Biopharma Applications." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185423.

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Анотація:
Physics-Informed Neural Networks (PINNs) are hybrid models that incorporate differential equations into the training of neural networks, with the aim of bringing the best of both worlds. This project used a mathematical model describing a Continuous Stirred-Tank Reactor (CSTR), to test two possible applications of PINNs. The first type of PINN was trained to predict an unknown reaction rate law, based only on the differential equation and a time series of the reactor state. The resulting model was used inside a multi-step solver to simulate the system state over time. The results showed that the PINN could accurately model the behaviour of the missing physics also for new initial conditions. However, the model suffered from extrapolation error when tested on a larger reactor, with a much lower reaction rate. Comparisons between using a numerical derivative or automatic differentiation in the loss equation, indicated that the latter had a higher robustness to noise. Thus, it is likely the best choice for real applications. A second type of PINN was trained to forecast the system state one-step-ahead based on previous states and other known model parameters. An ordinary feed-forward neural network with an equal architecture was used as baseline. The second type of PINN did not outperform the baseline network. Further studies are needed to conclude if or when physics-informed loss should be used in autoregressive applications.
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Wang, Jianxun. "Physics-Informed, Data-Driven Framework for Model-Form Uncertainty Estimation and Reduction in RANS Simulations." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/77035.

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Анотація:
Computational fluid dynamics (CFD) has been widely used to simulate turbulent flows. Although an increased availability of computational resources has enabled high-fidelity simulations (e.g. large eddy simulation and direct numerical simulation) of turbulent flows, the Reynolds-Averaged Navier-Stokes (RANS) equations based models are still the dominant tools for industrial applications. However, the predictive capability of RANS models is limited by potential inaccuracies driven by hypotheses in the Reynolds stress closure. With the ever-increasing use of RANS simulations in mission-critical applications, the estimation and reduction of model-form uncertainties in RANS models have attracted attention in the turbulence modeling community. In this work, I focus on estimating uncertainties stemming from the RANS turbulence closure and calibrating discrepancies in the modeled Reynolds stresses to improve the predictive capability of RANS models. Both on-line and off-line data are utilized to achieve this goal. The main contributions of this dissertation can be summarized as follows: First, a physics-based, data-driven Bayesian framework is developed for estimating and reducing model-form uncertainties in RANS simulations. An iterative ensemble Kalman method is employed to assimilate sparse on-line measurement data and empirical prior knowledge for a full-field inversion. The merits of incorporating prior knowledge and physical constraints in calibrating RANS model discrepancies are demonstrated and discussed. Second, a random matrix theoretic framework is proposed for estimating model-form uncertainties in RANS simulations. Maximum entropy principle is employed to identify the probability distribution that satisfies given constraints but without introducing artificial information. Objective prior perturbations of RANS-predicted Reynolds stresses in physical projections are provided based on comparisons between physics-based and random matrix theoretic approaches. Finally, a physics-informed, machine learning framework towards predictive RANS turbulence modeling is proposed. The functional forms of model discrepancies with respect to mean flow features are extracted from the off-line database of closely related flows based on machine learning algorithms. The RANS-modeled Reynolds stresses of prediction flows can be significantly improved by the trained discrepancy function, which is an important step towards the predictive turbulence modeling.
Ph. D.
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Частини книг з теми "Physics-informed Machine Learning"

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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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Cross, Elizabeth J., S. J. Gibson, M. R. Jones, D. J. Pitchforth, S. Zhang, and T. J. Rogers. "Physics-Informed Machine Learning for Structural Health Monitoring." In Structural Integrity, 347–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81716-9_17.

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Sun, Alexander Y., Hongkyu Yoon, Chung-Yan Shih, and Zhi Zhong. "Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey." In Knowledge-Guided Machine Learning, 111–32. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-5.

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Mo, Zhaobin, Yongjie Fu, Daran Xu, and Xuan Di. "TrafficFlowGAN: Physics-Informed Flow Based Generative Adversarial Network for Uncertainty Quantification." In Machine Learning and Knowledge Discovery in Databases, 323–39. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_20.

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Uhrich, Benjamin, Martin Schäfer, Oliver Theile, and Erhard Rahm. "Using Physics-Informed Machine Learning to Optimize 3D Printing Processes." In Progress in Digital and Physical Manufacturing, 206–21. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33890-8_18.

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Sankaran, Sathish, and Hardik Zalavadia. "Hybrid Data-Driven and Physics-Informed Reservoir Modeling for Unconventional Reservoirs." In Machine Learning Applications in Subsurface Energy Resource Management, 143–64. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003207009-12.

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Martín-González, Elena, Ebraham Alskaf, Amedeo Chiribiri, Pablo Casaseca-de-la-Higuera, Carlos Alberola-López, Rita G. Nunes, and Teresa Correia. "Physics-Informed Self-supervised Deep Learning Reconstruction for Accelerated First-Pass Perfusion Cardiac MRI." In Machine Learning for Medical Image Reconstruction, 86–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88552-6_9.

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Pateras, Joseph, Ashwin Vaidya, and Preetam Ghosh. "Physics-Informed Bias Method for Multiphysics Machine Learning: Reduced Order Amyloid-β Fibril Aggregation." In Recent Advances in Mechanics and Fluid-Structure Interaction with Applications, 157–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14324-3_7.

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Dat, Tran Tien, Yasunao Matsumoto, and Ji Dang. "A Preliminary Study on Physics-Informed Machine Learning-Based Structure Health Monitoring for Beam Structures." In Lecture Notes in Civil Engineering, 490–99. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39117-0_50.

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Ibrahim, Abdul Qadir, Sebastian Götschel, and Daniel Ruprecht. "Parareal with a Physics-Informed Neural Network as Coarse Propagator." In Euro-Par 2023: Parallel Processing, 649–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39698-4_44.

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Анотація:
AbstractParallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable expensive fine propagator provides accuracy. Typically, the coarse method is a numerical integrator using lower resolution, reduced order or a simplified model. Our paper proposes to use a physics-informed neural network (PINN) instead. We demonstrate for the Black-Scholes equation, a partial differential equation from computational finance, that Parareal with a PINN coarse propagator provides better speedup than a numerical coarse propagator. Training and evaluating a neural network are both tasks whose computing patterns are well suited for GPUs. By contrast, mesh-based algorithms with their low computational intensity struggle to perform well. We show that moving the coarse propagator PINN to a GPU while running the numerical fine propagator on the CPU further improves Parareal’s single-node performance. This suggests that integrating machine learning techniques into parallel-in-time integration methods and exploiting their differences in computing patterns might offer a way to better utilize heterogeneous architectures.
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Тези доповідей конференцій з теми "Physics-informed Machine Learning"

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Manasipov, Roman, Denis Nikolaev, Dmitrii Didenko, Ramez Abdalla, and Michael Stundner. "Physics Informed Machine Learning for Production Forecast." In SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212666-ms.

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Анотація:
Abstract Understanding the reservoir behavior is vital knowledge required for various aspects of the reservoir management cycle such as production optimization and establishment of the field development strategy. Reservoir simulation is the most accurate tool for production forecast, but often it is very expensive from aspects of computational time and investment in the model building process. In this work, the machine learning methods for accurate production forecast that honor the material balance constraints are presented. The presented hybrid model approach consists of several main components. The material balance constraints are necessary during the training process to avoid unphysical solutions and to honor conservation laws. For this reason, the Capacitance Resistance Model (CRM) was chosen due to its intuitive form and flexibility in describing reservoirs of various complexities. Another part of the solution is represented by powerful machine learning methods such as Generalized Additive Models (GAM), Gradient Boosting, and Convolutional and Recurrent Neural Networks. Neural Networks and Gradient Boosting methods are very popular machine learning techniques. However, in this work, it is demonstrated that GAM can also produce results comparable to the former methods while holding additional attractive properties. The basis functions of GAM are the splines, which are smooth functions with continuous derivatives. Such properties are very useful for optimization tasks. GAM is an extension of standard Generalized Linear Models (GLM), which provides rich tools for model explainability. It is hence also advantageous for the understanding how the reservoir behaves through such models. The implemented approach was applied to the publicly available data with an existing history matched reservoir model for the offshore field with several injectors and producers. This allowed us to compare results and build machine learning models that describe communication between wells and can be further analyzed though the simulation model. Machine learning methods are constantly improving at solving difficult problems, while it often suffers from nonphysical solutions and unexplainable models. The presented method holds the properties of explainable regression models while providing powerful predictability capabilities within material balance constraints. By no means does it try to replace the reservoir simulation but offers a complementary solution, which is reliable and necessary in cases where there is no full reservoir model available.
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Baseman, Elisabeth, Nathan Debardeleben, Sean Blanchard, Juston Moore, Olena Tkachenko, Kurt Ferreira, Taniya Siddiqua, and Vilas Sridharan. "Physics-Informed Machine Learning for DRAM Error Modeling." In 2018 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT). IEEE, 2018. http://dx.doi.org/10.1109/dft.2018.8602983.

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3

Kutz, Nathan, Diya Sashidhar, Shervin Sahba, Steven L. Brunton, Austin McDaniel, and Christopher Wilcox. "Physics-informed machine-learning for modeling aero-optics." In Applied Optical Metrology IV, edited by Erik Novak, James D. Trolinger, and Christopher C. Wilcox. SPIE, 2021. http://dx.doi.org/10.1117/12.2596540.

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Kim, Junyung, Asad Ullah Shah, Hyun Kang, and Xingang Zhao. "Physics-Informed Machine Learning-Aided System Space Discretization." In 12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021). Illinois: American Nuclear Society, 2021. http://dx.doi.org/10.13182/t124-34648.

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Tetali, Harsha Vardhan, K. Supreet Alguri, and Joel B. Harley. "Wave Physics Informed Dictionary Learning In One Dimension." In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2019. http://dx.doi.org/10.1109/mlsp.2019.8918835.

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Ghosh, Abantika, Mohannad Elhamod, Jie Bu, Wei-Cheng Lee, Anuj Karpatne, and Viktor A. Podolskiy. "Physics-Informed Machine Learning of Optical Modes in Composites." In CLEO: QELS_Fundamental Science. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleo_qels.2022.ftu1b.1.

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Анотація:
We present machine learning techniques that incorporate physics into the training process. We demonstrate, on example of predicting light propagation in multilayered composites, that physics-informed models are significantly more robust than their black box counterparts.
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Wong, Benjamin, Murali Damodaran, and Boo Cheong Khoo. "Physics-Informed Machine Learning for Inverse Airfoil Shape Design." In AIAA AVIATION 2023 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2023. http://dx.doi.org/10.2514/6.2023-4374.

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Leiteritz, Raphael, Marcel Hurler, and Dirk Pfluger. "Learning Free-Surface Flow with Physics-Informed Neural Networks." In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021. http://dx.doi.org/10.1109/icmla52953.2021.00266.

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Santos, Rogerio, Ijar DA FONSECA, and Domingos Rade. "Physics Informed Machine Learning for Path Planning of Space Robots." In XIX International Symposium on Dynamic Problems of Mechanics. ABCM, 2023. http://dx.doi.org/10.26678/abcm.diname2023.din2023-0030.

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Huber, Lilach Goren, Thomas Palmé, and Manuel Arias Chao. "Physics-Informed Machine Learning for Predictive Maintenance: Applied Use-Cases." In 2023 10th IEEE Swiss Conference on Data Science (SDS). IEEE, 2023. http://dx.doi.org/10.1109/sds57534.2023.00016.

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Звіти організацій з теми "Physics-informed Machine Learning"

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Martinez, Carianne, Jessica Jones, Drew Levin, Nathaniel Trask, and Patrick Finley. Physics-Informed Machine Learning for Epidemiological Models. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1706217.

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Wang, Jianxun, Jinlong Wu, Julia Ling, Gianluca Iaccarino, and Heng Xiao. Physics-Informed Machine Learning for Predictive Turbulence Modeling: Towards a Complete Framework. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1562229.

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Ullrich, Paul, Tapio Schneider, and Da Yang. Physics-Informed Machine Learning from Observations for Clouds, Convection, and Precipitation Parameterizations and Analysis. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769762.

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Ghanshyam, Pilania, Kenneth James McClellan, Christopher Richard Stanek, and Blas P. Uberuaga. Physics-Informed Machine Learning for Discovery and Optimization of Materials: A Case Study of Scintillators. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1463529.

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Bao, Jie, Chao Wang, Zhijie Xu, and Brian J. Koeppel. Physics-Informed Machine Learning with Application to Solid Oxide Fuel Cell System Modeling and Optimization. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1569289.

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Fan, Jiwen, Zhangshuan Hou, Paul O'Gorman, Jessika Trancik, John Allen, Peeyush Kumar, Ranveer Chandra, Jingyu Wang, and Lai-Yung Leung. Develop a weather-aware climate model to understand and predict extremes and associated power outages and renewable energy shortageswith uncertainty-aware and physics-informed machine learning. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769695.

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