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

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

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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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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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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8

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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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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9

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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10

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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11

Howland, Michael F., and John O. Dabiri. "Wind Farm Modeling with Interpretable Physics-Informed Machine Learning." Energies 12, no. 14 (July 16, 2019): 2716. http://dx.doi.org/10.3390/en12142716.

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Turbulent wakes trailing utility-scale wind turbines reduce the power production and efficiency of downstream turbines. Thorough understanding and modeling of these wakes is required to optimally design wind farms as well as control and predict their power production. While low-order, physics-based wake models are useful for qualitative physical understanding, they generally are unable to accurately predict the power production of utility-scale wind farms due to a large number of simplifying assumptions and neglected physics. In this study, we propose a suite of physics-informed statistical models to accurately predict the power production of arbitrary wind farm layouts. These models are trained and tested using five years of historical one-minute averaged operational data from the Summerview wind farm in Alberta, Canada. The trained models reduce the prediction error compared both to a physics-based wake model and a standard two-layer neural network. The trained parameters of the statistical models are visualized and interpreted in the context of the flow physics of turbulent wind turbine wakes.

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12

Tartakovsky, A. M., D. A. Barajas-Solano, and Q. He. "Physics-informed machine learning with conditional Karhunen-Loève expansions." Journal of Computational Physics 426 (February 2021): 109904. http://dx.doi.org/10.1016/j.jcp.2020.109904.

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13

Hsu, Abigail, Baolian Cheng, and Paul A. Bradley. "Analysis of NIF scaling using physics informed machine learning." Physics of Plasmas 27, no. 1 (January 2020): 012703. http://dx.doi.org/10.1063/1.5130585.

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14

Karpov, Platon I., Chengkun Huang, Iskandar Sitdikov, Chris L. Fryer, Stan Woosley, and Ghanshyam Pilania. "Physics-informed Machine Learning for Modeling Turbulence in Supernovae." Astrophysical Journal 940, no. 1 (November 1, 2022): 26. http://dx.doi.org/10.3847/1538-4357/ac88cc.

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Abstract Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSNe), but current simulations must rely on subgrid models, since direct numerical simulation is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, machine learning (ML) has shown an impressive predictive capability for calculating turbulence closure. We have developed a physics-informed convolutional neural network to preserve the realizability condition of the Reynolds stress that is necessary for accurate turbulent pressure prediction. The applicability of the ML subgrid model is tested here for magnetohydrodynamic turbulence in both the stationary and dynamic regimes. Our future goal is to utilize this ML methodology (available on GitHub) in the CCSN framework to investigate the effects of accurately modeled turbulence on the explosion of these stars.

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15

Tetali, Harsha Vardhan, and Joel Harley. "A physics-informed machine learning based dispersion curve estimation for non-homogeneous media." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A239. http://dx.doi.org/10.1121/10.0016136.

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Modern machine learning has been on the rise in many scientific domains, such as acoustics. Many scientific problems face challenges with limited data, which prevent the use of the many powerful machine learning strategies. In response, the physics of wave-propagation can be exploited to reduce the amount of data necessary and improve performance of machine learning techniques. Based on this need, we present a physics-informed machine learning framework, known as wave-informed regression, to extract dispersion curves from a guided wave wavefield data from non-homogeneous media. Wave-informed regression blends matrix factorization with known wave-physics by borrowing results from optimization theory. We briefly derive the algorithm and discuss a signal processing-based interpretability aspect of it, which aids in extracting dispersion curves for non-homogenous media. We show our results on a non-homogeneous media, where the dispersion curves change as a function of space. We demonstrate our ability to use wave-informed regression to extract spatially local dispersion curves.

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16

Kutz, J. Nathan, and Steven L. Brunton. "Parsimony as the ultimate regularizer for physics-informed machine learning." Nonlinear Dynamics 107, no. 3 (January 20, 2022): 1801–17. http://dx.doi.org/10.1007/s11071-021-07118-3.

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17

Corson, Gregory, Jaydeep Karandikar, and Tony Schmitz. "Physics-informed Bayesian machine learning case study: Integral blade rotors." Journal of Manufacturing Processes 85 (January 2023): 503–14. http://dx.doi.org/10.1016/j.jmapro.2022.12.004.

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18

Meguerdijian, Saro, Rajesh J. Pawar, Bailian Chen, Carl W. Gable, Terry A. Miller, and Birendra Jha. "Physics-informed machine learning for fault-leakage reduced-order modeling." International Journal of Greenhouse Gas Control 125 (May 2023): 103873. http://dx.doi.org/10.1016/j.ijggc.2023.103873.

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19

Sharma, Pushan, Wai Tong Chung, Bassem Akoush, and Matthias Ihme. "A Review of Physics-Informed Machine Learning in Fluid Mechanics." Energies 16, no. 5 (February 28, 2023): 2343. http://dx.doi.org/10.3390/en16052343.

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Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even replacing—high-fidelity numerical simulations of complex turbulent flows, which are often expensive due to the requirement of high temporal and spatial resolution. In this review, we (i) provide an introduction and historical perspective of ML methods, in particular neural networks (NN), (ii) examine existing PIML applications to fluid mechanics problems, especially in complex high Reynolds number flows, (iii) demonstrate the utility of PIML techniques through a case study, and (iv) discuss the challenges and opportunities of developing PIML for fluid mechanics.

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20

Zeng, Shi, and Dechang Pi. "Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning." Sensors 23, no. 10 (May 22, 2023): 4969. http://dx.doi.org/10.3390/s23104969.

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Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based surface roughness prediction methods to local minima may lead to poor model generalization or results that violate existing physical laws. Therefore, this paper combined physical knowledge with deep learning to propose a physics-informed deep learning method (PIDL) for milling surface roughness predictions under the constraints of physical laws. This method introduced physical knowledge in the input phase and training phase of deep learning. Data augmentation was performed on the limited experimental data by constructing surface roughness mechanism models with tolerable accuracy prior to training. In the training, a physically guided loss function was constructed to guide the training process of the model with physical knowledge. Considering the excellent feature extraction capability of convolutional neural networks (CNNs) and gated recurrent units (GRUs) in the spatial and temporal scales, a CNN–GRU model was adopted as the main model for milling surface roughness predictions. Meanwhile, a bi-directional gated recurrent unit and a multi-headed self-attentive mechanism were introduced to enhance data correlation. In this paper, surface roughness prediction experiments were conducted on the open-source datasets S45C and GAMHE 5.0. In comparison with the results of state-of-the-art methods, the proposed model has the highest prediction accuracy on both datasets, and the mean absolute percentage error on the test set was reduced by 3.029% on average compared to the best comparison method. Physical-model-guided machine learning prediction methods may be a future pathway for machine learning evolution.

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21

Omar, Sara Ibrahim, Chen Keasar, Ariel J. Ben-Sasson, and Eldad Haber. "Protein Design Using Physics Informed Neural Networks." Biomolecules 13, no. 3 (March 1, 2023): 457. http://dx.doi.org/10.3390/biom13030457.

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The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been successful in generating functional sequences, outperforming previous energy function-based methods. However, these machine learning methods are limited in their interoperability and robustness, especially when designing proteins that must function under non-ambient conditions, such as high temperature, extreme pH, or in various ionic solvents. To address this issue, we propose a new Physics-Informed Neural Networks (PINNs)-based protein sequence design approach. Our approach combines all-atom molecular dynamics simulations, a PINNs MD surrogate model, and a relaxation of binary programming to solve the protein design task while optimizing both energy and the structural stability of proteins. We demonstrate the effectiveness of our design framework in designing proteins that can function under non-ambient conditions.

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22

Manzoor, Tayyab, Hailong Pei, Zhongqi Sun, and Zihuan Cheng. "Model Predictive Control Technique for Ducted Fan Aerial Vehicles Using Physics-Informed Machine Learning." Drones 7, no. 1 (December 21, 2022): 4. http://dx.doi.org/10.3390/drones7010004.

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This paper proposes a model predictive control (MPC) approach for ducted fan aerial robots using physics-informed machine learning (ML), where the task is to fully exploit the capabilities of the predictive control design with an accurate dynamic model by means of a hybrid modeling technique. For this purpose, an indigenously developed ducted fan miniature aerial vehicle with adequate flying capabilities is used. The physics-informed dynamical model is derived offline by considering the forces and moments acting on the platform. On the basis of the physics-informed model, a data-driven ML approach called adaptive sparse identification of nonlinear dynamics is utilized for model identification, estimation, and correction online. Thereafter, an MPC-based optimization problem is computed by updating the physics-informed states with the physics-informed ML model at each step, yielding an effective control performance. Closed-loop stability and recursive feasibility are ensured under sufficient conditions. Finally, a simulation study is conducted to concisely corroborate the efficacy of the presented framework.

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23

Kashinath, K., M. Mustafa, A. Albert, J.-L. Wu, C. Jiang, S. Esmaeilzadeh, K. Azizzadenesheli, et al. "Physics-informed machine learning: case studies for weather and climate modelling." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2194 (February 15, 2021): 20200093. http://dx.doi.org/10.1098/rsta.2020.0093.

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Machine learning (ML) provides novel and powerful ways of accurately and efficiently recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio-temporal evolution of weather and climate processes. Off-the-shelf ML models, however, do not necessarily obey the fundamental governing laws of physical systems, nor do they generalize well to scenarios on which they have not been trained. We survey systematic approaches to incorporating physics and domain knowledge into ML models and distill these approaches into broad categories. Through 10 case studies, we show how these approaches have been used successfully for emulating, downscaling, and forecasting weather and climate processes. The accomplishments of these studies include greater physical consistency, reduced training time, improved data efficiency, and better generalization. Finally, we synthesize the lessons learned and identify scientific, diagnostic, computational, and resource challenges for developing truly robust and reliable physics-informed ML models for weather and climate processes.This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

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24

Wenzel, Sören, Elena Slomski-Vetter, and Tobias Melz. "Optimizing System Reliability in Additive Manufacturing Using Physics-Informed Machine Learning." Machines 10, no. 7 (June 29, 2022): 525. http://dx.doi.org/10.3390/machines10070525.

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Fused filament fabrication (FFF), an additive manufacturing process, is an emerging technology with issues in the uncertainty of mechanical properties and quality of printed parts. The consideration of all main and interaction effects when changing print parameters is not efficiently feasible, due to existing stochastic dependencies. To address this issue, a machine learning method is developed to increase reliability by optimizing input parameters and predicting system responses. A structure of artificial neural networks (ANN) is proposed that predicts a system response based on input parameters and observations of the system and similar systems. In this way, significant input parameters for a reliable system can be determined. The ANN structure is part of physics-informed machine learning and is pretrained with domain knowledge (DK) to require fewer observations for full training. This includes theoretical knowledge of idealized systems and measured data. New predictions for a system response can be made without retraining but by using further observations from the predicted system. Therefore, the predictions are available in real time, which is a precondition for the use in industrial environments. Finally, the application of the developed method to print bed adhesion in FFF and the increase in system reliability are discussed and evaluated.

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25

Fang, Dehong, and Jifu Tan. "Immersed boundary-physics informed machine learning approach for fluid–solid coupling." Ocean Engineering 263 (November 2022): 112360. http://dx.doi.org/10.1016/j.oceaneng.2022.112360.

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26

Raymond, Samuel J., David Collins, and John Willams. "Designing acoustofluidic devices using a simplified physics-informed machine learning approach." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A254. http://dx.doi.org/10.1121/10.0011237.

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The safe positioning of particles within an acoustofluidic device is critical in biomedical and biological applications. Relating the design of acoustofluidic device walls and the internal acoustic field is a complex, nonlinear problem. The field of Physics-Informed Machine Learning (PIML) offers a number of potential approaches to simplify the design of these devices. One such PIML approach is learning from synthetic data. With large scientific data sets with rich spatial-temporal data and high-performance computing providing large amounts of data to be inferred and interpreted, the task of PIML is to ensure that these predictions and inferences are enforced by, and conform to the limits imposed by physical laws. The tools employed in PIML can include large, deep neural networks, Bayesian modeling, and deep reinforcement learning with sophisticated simulations of the environment. In this work, we show a simplified version of PIML using a combination of a small fully connected neural network and a 2D meshfree simulator of acoustic devices to predict the boundary shape for an acoustically actuated device. We will discuss the real-world results and applications, as well as the current limitations of this approach and the path ahead to scale and include more complexity for more applications and designs.

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27

Chen, Wenqian, Qian Wang, Jan S. Hesthaven, and Chuhua Zhang. "Physics-informed machine learning for reduced-order modeling of nonlinear problems." Journal of Computational Physics 446 (December 2021): 110666. http://dx.doi.org/10.1016/j.jcp.2021.110666.

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28

Miller, Scott T., John F. Lindner, Anshul Choudhary, Sudeshna Sinha, and William L. Ditto. "The scaling of physics-informed machine learning with data and dimensions." Chaos, Solitons & Fractals: X 5 (March 2020): 100046. http://dx.doi.org/10.1016/j.csfx.2020.100046.

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29

Srinivasan, Shriram, Eric Cawi, Jeffrey Hyman, Dave Osthus, Aric Hagberg, Hari Viswanathan, and Gowri Srinivasan. "Physics-informed machine learning for backbone identification in discrete fracture networks." Computational Geosciences 24, no. 3 (May 17, 2020): 1429–44. http://dx.doi.org/10.1007/s10596-020-09962-5.

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30

Zhang, Xinlei, Jinlong Wu, Olivier Coutier-Delgosha, and Heng Xiao. "Recent progress in augmenting turbulence models with physics-informed machine learning." Journal of Hydrodynamics 31, no. 6 (December 2019): 1153–58. http://dx.doi.org/10.1007/s42241-019-0089-y.

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31

Xie, Chiyu, Shuyi Du, Jiulong Wang, Junming Lao, and Hongqing Song. "Intelligent modeling with physics-informed machine learning for petroleum engineering problems." Advances in Geo-Energy Research 8, no. 2 (March 12, 2023): 71–75. http://dx.doi.org/10.46690/ager.2023.05.01.

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32

Ta, Hoa, Shi Wen Wong, Nathan McClanahan, Jung-Han Kimn, and Kaiqun Fu. "Exploration on Physics-Informed Neural Networks on Partial Differential Equations (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 16344–45. http://dx.doi.org/10.1609/aaai.v37i13.27032.

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Анотація:

Data-driven related solutions are dominating various scientific fields with the assistance of machine learning and data analytics. Finding effective solutions has long been discussed in the area of machine learning. The recent decade has witnessed the promising performance of the Physics-Informed Neural Networks (PINN) in bridging the gap between real-world scientific problems and machine learning models. In this paper, we explore the behavior of PINN in a particular range of different diffusion coefficients under specific boundary conditions. In addition, different initial conditions of partial differential equations are solved by applying the proposed PINN. Our paper illustrates how the effectiveness of the PINN can change under various scenarios. As a result, we demonstrate a better insight into the behaviors of the PINN and how to make the proposed method more robust while encountering different scientific and engineering problems.

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33

Pombo, Daniel Vázquez, Peder Bacher, Charalampos Ziras, Henrik W. Bindner, Sergiu V. Spataru, and Poul E. Sørensen. "Benchmarking physics-informed machine learning-based short term PV-power forecasting tools." Energy Reports 8 (November 2022): 6512–20. http://dx.doi.org/10.1016/j.egyr.2022.05.006.

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34

Soriano, Mario A., Helen G. Siegel, Nicholaus P. Johnson, Kristina M. Gutchess, Boya Xiong, Yunpo Li, Cassandra J. Clark, Desiree L. Plata, Nicole C. Deziel, and James E. Saiers. "Assessment of groundwater well vulnerability to contamination through physics-informed machine learning." Environmental Research Letters 16, no. 8 (July 22, 2021): 084013. http://dx.doi.org/10.1088/1748-9326/ac10e0.

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35

Shih, Yueh-Ting, Yunfeng Shi, and Liping Huang. "Predicting glass properties by using physics- and chemistry-informed machine learning models." Journal of Non-Crystalline Solids 584 (May 2022): 121511. http://dx.doi.org/10.1016/j.jnoncrysol.2022.121511.

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36

Lakshminarayana, Subhash, Saurav Sthapit, and Carsten Maple. "Application of Physics-Informed Machine Learning Techniques for Power Grid Parameter Estimation." Sustainability 14, no. 4 (February 11, 2022): 2051. http://dx.doi.org/10.3390/su14042051.

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Power grid parameter estimation involves the estimation of unknown parameters, such as the inertia and damping coefficients, from the observed dynamics. In this work, we present physics-informed machine learning algorithms for the power system parameter estimation problem. First, we propose a novel algorithm to solve the parameter estimation based on the Sparse Identification of Nonlinear Dynamics (SINDy) approach, which uses sparse regression to infer the parameters that best describe the observed data. We then compare its performance against another benchmark algorithm, namely, the physics-informed neural networks (PINN) approach applied to parameter estimation. We perform extensive simulations on IEEE bus systems to examine the performance of the aforementioned algorithms. Our results show that the SINDy algorithm outperforms the PINN algorithm in estimating the power grid parameters over a wide range of system parameters (including high and low inertia systems) and power grid architectures. Particularly, in case of the slow dynamics system, the proposed SINDy algorithms outperforms the PINN algorithm, which struggles to accurately determine the parameters. Moreover, it is extremely efficient computationally and so takes significantly less time than the PINN algorithm, thus making it suitable for real-time parameter estimation. Furthermore, we present an extension of the SINDy algorithm to a scenario where the operator does not have the exact knowledge of the underlying system model. We also present a decentralised implementation of the SINDy algorithm which only requires limited information exchange between the neighbouring nodes of a power grid.

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37

Mudunuru, M. K., and S. Karra. "Physics-informed machine learning models for predicting the progress of reactive-mixing." Computer Methods in Applied Mechanics and Engineering 374 (February 2021): 113560. http://dx.doi.org/10.1016/j.cma.2020.113560.

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38

Shan, Liqun, Chengqian Liu, Yanchang Liu, Yazhou Tu, Linyu Deng, and Xiali Hei. "Physics-informed machine learning for solving partial differential equations in porous media." Advances in Geo-Energy Research 8, no. 1 (January 10, 2023): 37–44. http://dx.doi.org/10.46690/ager.2023.04.04.

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39

Edwards, Chris. "Neural networks learn to speed up simulations." Communications of the ACM 65, no. 5 (April 2022): 27–29. http://dx.doi.org/10.1145/3524015.

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40

Schröder, Laura, Nikolay Krasimirov Dimitrov, David Robert Verelst, and John Aasted Sørensen. "Using Transfer Learning to Build Physics-Informed Machine Learning Models for Improved Wind Farm Monitoring." Energies 15, no. 2 (January 13, 2022): 558. http://dx.doi.org/10.3390/en15020558.

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Анотація:

This paper introduces a novel, transfer-learning-based approach to include physics into data-driven normal behavior monitoring models which are used for detecting turbine anomalies. For this purpose, a normal behavior model is pretrained on a large simulation database and is recalibrated on the available SCADA data via transfer learning. For two methods, a feed-forward artificial neural network (ANN) and an autoencoder, it is investigated under which conditions it can be helpful to include simulations into SCADA-based monitoring systems. The results show that when only one month of SCADA data is available, both the prediction accuracy as well as the prediction robustness of an ANN are significantly improved by adding physics constraints from a pretrained model. As the autoencoder reconstructs the power from itself, it is already able to accurately model the normal behavior power. Therefore, including simulations into the model does not improve its prediction performance and robustness significantly. The validation of the physics-informed ANN on one month of raw SCADA data shows that it is able to successfully detect a recorded blade angle anomaly with an improved precision due to fewer false positives compared to its purely SCADA data-based counterpart.

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41

Arifuzzaman, S. M., Kejun Dong, and Aibing Yu. "Process model of vibrating screen based on DEM and physics-informed machine learning." Powder Technology 410 (September 2022): 117869. http://dx.doi.org/10.1016/j.powtec.2022.117869.

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42

Borrel-Jensen, Nikolas, Allan P. Engsig-Karup, and Cheol-Ho Jeong. "Machine learning-based room acoustics using flow maps and physics-informed neural networks." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A232—A233. http://dx.doi.org/10.1121/10.0011164.

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The development of efficient and accurate numerical methods for simulating realistic sound in virtual environments—such as computer games and VR/AR—has been an active research area for the last decades. However, handling dynamic scenes with many moving sources is still challenging due to intractable storage requirements and extensive computation time. A recently proposed physics-informed neural network (PINN) approach learns a compact and efficient surrogate model with parameterized moving sources and impedance boundaries on a grid-less 1-D domain. Contrary to traditional “black-box” deep learning, PINNs minimize the residuals of the governing equations through the loss function. We will extend this work using flow maps implemented as Residual Networks (ResNets). ResNets are interpreted from a dynamic systems perspective as ordinary differential equations that can be used as building blocks to approximate the governing equations in time. We will examine the pros and cons of ResNets in acoustics and compare them with state-of-the-art numerical methods and vanilla feed-forward neural networks in terms of accuracy and efficiency.

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43

Guo, Shenghan, Mohit Agarwal, Clayton Cooper, Qi Tian, Robert X. Gao, Weihong Guo, and Y. B. Guo. "Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm." Journal of Manufacturing Systems 62 (January 2022): 145–63. http://dx.doi.org/10.1016/j.jmsy.2021.11.003.

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44

Sepe, Marzia, Antonino Graziano, Maciej Badora, Alessandro Di Stazio, Luca Bellani, Michele Compare, and Enrico Zio. "A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets." Journal of the Global Power and Propulsion Society, May (May 25, 2021): 1–15. http://dx.doi.org/10.33737/jgpps/134845.

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The paper presents an overview of Baker Hughes digital framework for a predictive maintenance service boosted by Machine Learning and asset knowledge, applied to turbomachinery assets. Optimization of the maintenance scenario is performed through a risk model that assesses online health status and probability of failure, by detecting functional anomalies and aging phenomena and evaluating their impact on asset serviceability. Turbomachinery domain knowledge is used to create physics-based models, to configure a severity assessment layer and to properly map maintenance actions to anomaly types. The implemented analytics framework is able also to forecast engine behaviour over the future in order to optimize asset operation and maintenance, minimizing downtime and residual risk. Predictive capabilities are optimized thanks to the hybrid approach, where physics-based knowledge empowers long term prediction accuracy while data-driven analytics ensure fast-events prognostics. Accuracy of the hybrid approach is a differentiator for maintenance optimization, allowing activities to be planned properly and in early advance with respect to outage execution.

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45

Cotter, Emma, Christopher Bassett, and Andone Lavery. "Classification of broadband target spectra in the mesopelagic using physics-informed machine learning." Journal of the Acoustical Society of America 149, no. 6 (June 2021): 3889–901. http://dx.doi.org/10.1121/10.0005114.

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46

Du, Y., T. Mukherjee, and T. DebRoy. "Physics-informed machine learning and mechanistic modeling of additive manufacturing to reduce defects." Applied Materials Today 24 (September 2021): 101123. http://dx.doi.org/10.1016/j.apmt.2021.101123.

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47

Kim, Kyung Mo, Paul Hurley, and Juliana Pacheco Duarte. "Physics-informed machine learning-aided framework for prediction of minimum film boiling temperature." International Journal of Heat and Mass Transfer 191 (August 2022): 122839. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2022.122839.

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48

Zhao, Ze, Michael Stuebner, Jim Lua, Nam Phan, and Jinhui Yan. "Full-field temperature recovery during water quenching processes via physics-informed machine learning." Journal of Materials Processing Technology 303 (May 2022): 117534. http://dx.doi.org/10.1016/j.jmatprotec.2022.117534.

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49

Qian, Elizabeth, Boris Kramer, Benjamin Peherstorfer, and Karen Willcox. "Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems." Physica D: Nonlinear Phenomena 406 (May 2020): 132401. http://dx.doi.org/10.1016/j.physd.2020.132401.

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50

Liu, Sen, Branden B. Kappes, Behnam Amin-ahmadi, Othmane Benafan, Xiaoli Zhang, and Aaron P. Stebner. "Physics-informed machine learning for composition – process – property design: Shape memory alloy demonstration." Applied Materials Today 22 (March 2021): 100898. http://dx.doi.org/10.1016/j.apmt.2020.100898.

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