Journal articles: '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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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.

12

Teurtrie, Adrien, Nathanaël Perraudin, Thomas Holvoet, Hui Chen, Duncan T. L. Alexander, Guillaume Obozinski, and Cécile Hébert. "Physics-Guided Machine Learning for the Analysis of Low SNR STEM-EDXS Data." Microscopy and Microanalysis 28, S1 (July 22, 2022): 2978–79. http://dx.doi.org/10.1017/s1431927622011163.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Buster, Grant, Mike Bannister, Aron Habte, Dylan Hettinger, Galen Maclaurin, Michael Rossol, Manajit Sengupta, and Yu Xie. "Physics-guided machine learning for improved accuracy of the National Solar Radiation Database." Solar Energy 232 (January 2022): 483–92. http://dx.doi.org/10.1016/j.solener.2022.01.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Jia, Xiaowei, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach, and Vipin Kumar. "Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles." ACM/IMS Transactions on Data Science 2, no. 3 (May 17, 2021): 1–26. http://dx.doi.org/10.1145/3447814.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Physics-based models are often used to study engineering and environmental systems. The ability to model these systems is the key to achieving our future environmental sustainability and improving the quality of human life. This article focuses on simulating lake water temperature, which is critical for understanding the impact of changing climate on aquatic ecosystems and assisting in aquatic resource management decisions. General Lake Model (GLM) is a state-of-the-art physics-based model used for addressing such problems. However, like other physics-based models used for studying scientific and engineering systems, it has several well-known limitations due to simplified representations of the physical processes being modeled or challenges in selecting appropriate parameters. While state-of-the-art machine learning models can sometimes outperform physics-based models given ample amount of training data, they can produce results that are physically inconsistent. This article proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improves the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models (by over 20% even with very little training data), while generating outputs consistent with physical laws. An important aspect of our PGRNN approach lies in its ability to incorporate the knowledge encoded in physics-based models. This allows training the PGRNN model using very few true observed data while also ensuring high prediction accuracy. Although we present and evaluate this methodology in the context of modeling the dynamics of temperature in lakes, it is applicable more widely to a range of scientific and engineering disciplines where physics-based (also known as mechanistic) models are used.

15

Hagmeyer, Simon, Peter Zeiler, and Marco F. Huber. "On the Integration of Fundamental Knowledge about Degradation Processes into Data-Driven Diagnostics and Prognostics Using Theory-Guided Data Science." PHM Society European Conference 7, no. 1 (June 29, 2022): 156–65. http://dx.doi.org/10.36001/phme.2022.v7i1.3352.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

In Prognostics and Health Management, there are three main approaches for implementing diagnostic and prognostic applications. These approaches are data-driven methods, physical model-based methods, and combinations of them, in the form of hybrid methods. Each of them has specific advantages but also limitations for their purposeful implementation. In the case of data-driven methods, one of the main limitations is the availability of sufficient training data that adequately cover the relevant state space. For model-based methods, on the other hand, it is often the case that the degradation process of the considered technical system is of significant complexity. In such a scenario physics-based modeling requires great effort or is not possible at all. Combinations of data-driven and model-based approaches in form of hybrid approaches offer the possibility to partially mitigate the shortcomings of the other two approaches, however, require a sufficiently detailed data-driven and physics-based model. This paper addresses the transitional field between data-driven and hybrid approaches. Despite the issues of formulating a physics-based model that provides a representation of the degradation process, basic knowledge of the considered system and of the laws governing its degradation process is usually available. Integration of such knowledge into a machine learning process is part of a research field that is either called theory-guided data science, (physics) informed machine learning, physics-based learning or physics guided machine learning. First, the state of research in Prognostics and Health Management on methods of this field is presented and existing research gaps are outlined. Then, a concept is introduced for incorporating fundamental knowledge, such as monotonicity constraints, into data-driven diagnostic and prognostic applications using approaches from theory-guided data science. A special aspect of this concept is its cross-application usability through the consideration of knowledge that repeatedly occurs in diagnostics and prognostics. This is, for example, knowledge about physically justified boundaries whose compliance makes a prediction of the data-driven model plausible in the first place.

16

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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.

17

Gurgen, Anil, and Nam T. Dinh. "Development and assessment of a reactor system prognosis model with physics-guided machine learning." Nuclear Engineering and Design 398 (November 2022): 111976. http://dx.doi.org/10.1016/j.nucengdes.2022.111976.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Piccione, A., J. W. Berkery, S. A. Sabbagh, and Y. Andreopoulos. "Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas." Nuclear Fusion 60, no. 4 (March 18, 2020): 046033. http://dx.doi.org/10.1088/1741-4326/ab7597.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Jurj, Sorin Liviu, Dominik Grundt, Tino Werner, Philipp Borchers, Karina Rothemann, and Eike Möhlmann. "Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning." Energies 14, no. 22 (November 12, 2021): 7572. http://dx.doi.org/10.3390/en14227572.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This paper presents a novel approach for improving the safety of vehicles equipped with Adaptive Cruise Control (ACC) by making use of Machine Learning (ML) and physical knowledge. More exactly, we train a Soft Actor-Critic (SAC) Reinforcement Learning (RL) algorithm that makes use of physical knowledge such as the jam-avoiding distance in order to automatically adjust the ideal longitudinal distance between the ego- and leading-vehicle, resulting in a safer solution. In our use case, the experimental results indicate that the physics-guided (PG) RL approach is better at avoiding collisions at any selected deceleration level and any fleet size when compared to a pure RL approach, proving that a physics-informed ML approach is more reliable when developing safe and efficient Artificial Intelligence (AI) components in autonomous vehicles (AVs).

20

Liang, Yu, and Dalei Wu. "Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction." Journal of Systemics, Cybernetics and Informatics 20, no. 5 (October 2022): 148–59. http://dx.doi.org/10.54808/jsci.20.05.148.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The COVID-19 pandemic has significantly impacted most countries in the world. Analyzing COVID-19 data from these countries together is a prominent challenge. Under the sponsorship of NSF REU, this paper describes our experience with a ten-week project that aims to guide an REU scholar to develop a physics-guided graph attention network to predict the global COVID- 19 Pandemics. We mainly presented the preparation, implementation, and dissemination of the addressed project. The COVID-19 situation in a country could be dramatically different from that of others, which suggests that COVID-19 pandemic data are generated based on different mechanisms, making COVID-19 data in different countries follow different probability distributions. Learning more than one hundred underlying probability distributions for countries in the world from large scale COVID- 19 data is beyond a single machine learning model. To address this challenge, we proposed two team-learning frameworks for predicting the COVID-19 pandemic trends: peer learning and layered ensemble learning framework. This addressed framework assigns an adaptive physics-guided graph attention network (GAT) to each learning agent. All the learning agents are fabricated in a hierarchical architecture, which enables agents to collaborate with each other in peer-to-peer and cross-layer way. This layered architecture shares the burden of large-scale data processing on machine learning models of all units. Experiments are run to verify the effectiveness of our approaches. The results indicate the proposed ensemble outperforms baseline methods. Besides being documented on GitHub, this work has resulted in two journal papers.

21

Cheng, Baolian, and Paul A. Bradley. "What Machine Learning Can and Cannot Do for Inertial Confinement Fusion." Plasma 6, no. 2 (June 1, 2023): 334–44. http://dx.doi.org/10.3390/plasma6020023.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.

22

Qin, Yue, Changyu Su, Dongdong Chu, Jicai Zhang, and Jinbao Song. "A Review of Application of Machine Learning in Storm Surge Problems." Journal of Marine Science and Engineering 11, no. 9 (September 1, 2023): 1729. http://dx.doi.org/10.3390/jmse11091729.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The rise of machine learning (ML) has significantly advanced the field of coastal oceanography. This review aims to examine the existing deficiencies in numerical predictions of storm surges and the effort that has been made to improve the predictive accuracy through the application of ML. The readers are guided through the steps required to implement ML algorithms, from the first step of formulating problems to data collection and determination of input features to model selection, development and evaluation. Additionally, the review explores the application of hybrid methods, which combine the bilateral advantages of data-driven methods and physics-based models. Furthermore, the strengths and limitations of ML methods in predicting storm surges are thoroughly discussed, and research gaps are identified. Finally, we outline a vision toward a trustworthy and reliable storm surge forecasting system by introducing novel physics-informed ML techniques. We are meant to provide a primer for beginners and experts in coastal ocean sciences who share a keen interest in ML methodologies in the context of storm surge problems.

23

Cunha, Barbara Zaparoli, Abdel-Malek Zine, Mohamed Ichchou, Christophe Droz, and Stéphane Foulard. "On Machine-Learning-Driven Surrogates for Sound Transmission Loss Simulations." Applied Sciences 12, no. 21 (October 23, 2022): 10727. http://dx.doi.org/10.3390/app122110727.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Surrogate models are data-based approximations of computationally expensive simulations that enable efficient exploration of the model’s design space and informed decision making in many physical domains. The usage of surrogate models in the vibroacoustic domain, however, is challenging due to the non-smooth, complex behavior of wave phenomena. This paper investigates four machine learning (ML) approaches in the modelling of surrogates of sound transmission loss (STL). Feature importance and feature engineering are used to improve the models’ accuracy while increasing their interpretability and physical consistency. The transfer of the proposed techniques to other problems in the vibroacoustic domain and possible limitations of the models are discussed. Experiments show that neural network surrogates with physics-guided features have better accuracy than other ML models across different STL models. Furthermore, sensitivity analysis methods are used to assess how physically coherent the analyzed surrogates are.

24

Wu, Xiaoqin, and Yipei Wang. "A physics-based machine learning approach for modeling the complex reflection coefficients of metal nanowires." Nanotechnology 33, no. 20 (February 21, 2022): 205701. http://dx.doi.org/10.1088/1361-6528/ac512e.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract Metal nanowires are attractive building blocks for next-generation plasmonic devices with high performance and compact footprint. The complex reflection coefficients of the plasmonic waveguides are crucial for estimation of the resonating, lasing, or sensing performance. By incorporating physics-guided objective functions and constraints, we propose a simple approach to convert the specific reflection problem of nanowires to a universal regression problem. Our approach is able to efficiently and reliably determine both the reflectivity and reflection phase of the metal nanowires with arbitrary geometry parameters, working environments, and terminal shapes, merging the merits of the physics-based modeling and the data-driven modeling. The results may provide valuable reference for building comprehensive datasets of plasmonic architectures, facilitating theoretical investigations and large-scale designs of nanophotonic components and devices.

25

Nguyen, Cong Tien, Selda Oterkus, and Erkan Oterkus. "A physics-guided machine learning model for two-dimensional structures based on ordinary state-based peridynamics." Theoretical and Applied Fracture Mechanics 112 (April 2021): 102872. http://dx.doi.org/10.1016/j.tafmec.2020.102872.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Wiedemann, Arthur, Christopher Fuller, and Kyle Pascioni. "Constructing a physics-guided machine learning neural network to predict tonal noise emitted by a propeller." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 264, no. 1 (June 24, 2022): 151–62. http://dx.doi.org/10.3397/nc-2022-709.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Artificial neural networks offer a highly nonlinear and adaptive model for predicting complex interactions between input-output parameters. However, these networks require large datasets which often exceed practical considerations in modeling experimental results. To alleviate the dataset size requirement, a method known as physics-guided machine learning has been applied to construct several neural networks for predicting propeller tonal noise in the time domain over a broad range of flight conditions. Three space-filling designs, namely, Latin-Hypercube, Sphere-Packing, and Grid-Space, were used to distribute points throughout the input parameter space encompassing nondimensional flight conditions and observer geometry. Each neural network's performance was validated by conditions outside of the training set and compared to the Propeller Analysis System tool from the NASA Aircraft Noise Prediction Program. The Latin-Hypercube and the Sphere-Packing designs provided a uniform representation of the domain, which improved the tonal noise prediction in comparison to the Grid design. The black-box nature of these neural networks was explored, and post-network functions were developed to remove discontinuities in the acoustic signal. Overall, the methods herein show a notable improvement in prediction performance in comparison to a multilayer perceptron. Additional loss functions are necessary for ensuring reasonable accuracy of predictive networks on small datasets and will be investigated for the final paper.

27

Liang, Lin, and Ting Lei. "Machine-Learning-Enabled Automatic Sonic Shear Processing." Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 62, no. 3 (June 1, 2021): 282–95. http://dx.doi.org/10.30632/pjv62n3-2021a3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Flexural-dipole sonic logging has been widely used as the primary method to measure formation shear slowness because it can be applied in both fast and slow formations and can resolve azimuthal anisotropy. The flexural-dipole waveforms are dispersive borehole-guided waves that are sensitive to borehole geometry, mud, and formation properties, and therefore the processing techniques need to honor the physical dispersive signatures to obtain an accurate estimation of shear slowness. Traditional processing techniques are based on either a model-dependent method, in which an isotropic model is used as a reference to compensate for the dispersion effect, or a model-independent method, which optimizes nonphysical parameters to fit a simplified model to the field dispersion data extracted in the slowness-frequency domain. Many methods often require inputs, such as mud slowness, frequency bandpass filter, or an initial guess of formation shear. Consequently, these methods often fail to interpret the dispersion signature properly when those inputs are inaccurate or when the waveform quality is poor due to downhole logging noises. The users must manually tune the processing parameters and/or choose different methods as a workaround, which causes extra time and effort to obtain the result, hence imposes a significant challenge for automating sonic shear processing. We developed a physics-driven, machine-learning-based method for enhancing the interpretation of borehole sonic dipole data for wireline logging in an openhole scenario. A synthetic database is generated from an anisotropic root-finding, mode-search routine and used to train a neural network model as an accurate and efficient proxy. This neural network model can be used for real-time sensitivity analysis and performing inversion to the measured sonic dipole dispersion data to estimate relevant model parameters with associated uncertainties. We introduce how this trained model can be used to enhance the labeling and extraction of the dipole dispersion mode. We developed a new method that outperforms previous model-dependent and model-independent approaches because the new method introduces a mechanism to constrain the solution with physics that also has the capability to incorporate more complicated physical dispersion signatures. This new method does not rely on a good initial guess on mud slowness and formation shear slowness, nor any tuning parameter. This leads to significant progress toward fully automated sonic interpretation. The algorithm has been tested on field data for challenging borehole and geological conditions.

28

Gulick, Walter. "“Rules of Rightness” and the Evolutionary Emergence of Purpose." Tradition and Discovery: The Polanyi Society Periodical 49, no. 1 (2023): 21–26. http://dx.doi.org/10.5840/traddisc20234914.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Michael Polanyi’s essay “Rules of Rightness” argues that for living beings, both machine-like embodied processes and informal purposeful operations are guided by standards of proper functioning. This article traces the origins of rules of rightness back to the concomitant rise of life and purpose in the universe. Thereby the deterministic control of all things by the laws of physics and chemistry is broken. Powered by an independent active principle and guided by three inarticulate modes of learning, life takes on increasingly complex expressions of purpose in evolutionary history. Along the way, purposeful informal operations make use of and often create contrivances that further the explosive telic growth of life.

29

Ko, Hyunwoong, Yan Lu, Zhuo Yang, Ndeye Y. Ndiaye, and Paul Witherell. "A framework driven by physics-guided machine learning for process-structure-property causal analytics in additive manufacturing." Journal of Manufacturing Systems 67 (April 2023): 213–28. http://dx.doi.org/10.1016/j.jmsy.2022.09.010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Zhang, Junru, Yang Liu, Durga Chandra Sekhar.P, Manjot Singh, Yuxin Tong, Ezgi Kucukdeger, Hu Young Yoon, et al. "Rapid, autonomous high-throughput characterization of hydrogel rheological properties via automated sensing and physics-guided machine learning." Applied Materials Today 30 (February 2023): 101720. http://dx.doi.org/10.1016/j.apmt.2022.101720.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Cao, Bin, Shuang Yang, Ankang Sun, Ziqiang Dong, and Tong-Yi Zhang. "Domain knowledge-guided interpretive machine learning: formula discovery for the oxidation behavior of ferritic-martensitic steels in supercritical water." Journal of Materials Informatics 2, no. 2 (2022): 4. http://dx.doi.org/10.20517/jmi.2022.04.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

A general formula with high generalization and accurate prediction power is highly desirable for science, technology and engineering. In addition to human beings, artificial intelligence algorithms show great promise for the discovery of formulas. In this study, we propose a domain knowledge-guided interpretive machine learning strategy and demonstrate it by studying the oxidation behavior of ferritic-martensitic steels in supercritical water. The oxidation Cr equivalent is, for the first time, proposed in the present work to represent all contributions of alloying elements to oxidation, derived by our domain knowledge and interpretive machine learning algorithms. An open-source tree classifier for linear regression algorithm is also, for the first time, developed to materialize the formula with collected data. This algorithm effectively captures the linear correlation between compositions, testing environments and oxidation behaviors from the data. The sure independence screening and sparsifying operator algorithm finally assembles the information derived from the tree classifier for linear regression algorithm, resulting in a general formula. The general formula with the determined parameters has the power to predict, quantitatively and accurately, the oxidation behavior of ferritic-martensitic steels with multiple alloying elements exposed to various supercritical water environments, thereby providing guidance for the design of anti-oxidation steels and hence promoting the development of power plants with improved safety. The present work demonstrates the power of domain knowledge-guided interpretive machine learning with respect to the data-driven discovery of physics-informed formulas and the acceleration of materials informatics development.

32

Rai, Rahul, and Chandan K. Sahu. "Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus." IEEE Access 8 (2020): 71050–73. http://dx.doi.org/10.1109/access.2020.2987324.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Dhara, Arnab, and Mrinal K. Sen. "Physics-guided deep autoencoder to overcome the need for a starting model in full-waveform inversion." Leading Edge 41, no. 6 (June 2022): 375–81. http://dx.doi.org/10.1190/tle41060375.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Full-waveform inversion (FWI) is a popular technique to obtain high-resolution estimates of earth model parameters using all information present in seismic data. Thus, it can provide important information about the subsurface. The FWI algorithm is formulated as a data-fitting minimization problem that iteratively updates an initial velocity model using the gradient of the misfit until an acceptable match is obtained between the real and synthetic data under a tolerance level based on noise in the data. The inversion is computationally expensive and can converge to a local minimum if the starting model used is not close enough to an optimal model. Here, we propose an alternative approach using a combination of machine learning and the physics of the forward model. Unlike conventional supervised machine learning, known answers are not required to train our network. The shot gathers are input to a convolutional neural network-based autoencoder, the output of which is used as the velocity model that is used to compute synthetic seismograms. The synthetic data are compared against observed input data, and the misfit is estimated. The gradient of the misfit with respect to the velocity model parameters is calculated using the adjoint state method. The adjoint state gradient is then used to update the network weights using the automatic differentiation technique. Once the misfit term converges, the neural network can generate velocity models consistent with the observed data. We observe that the neural network can capture spatial correlations at different scales and thus can introduce regularization in our inverse problem. Experiments with the Marmousi model and SEG Advanced Modeling Corporation Phase 1 salt model suggest that the proposed method can overcome local minima, requires no starting model, and produces robust results in the presence of noise and complex salt body structures.

34

Fadziso, Takudzwa. "Enhancing Predictions in Ungauged Basins Using Machine Learning to Its Full Potential." Asian Journal of Applied Science and Engineering 8, no. 1 (May 5, 2019): 35–50. http://dx.doi.org/10.18034/ajase.v8i1.10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

In ungauged basins, long short-term memory (LSTM) networks provide unparalleled precision in prediction. Using k-fold validation, we trained and tested various LSTMs on 531 basins from the CAMELS data set, allowing us to make predictions in basins with no training data. The training and test data set contained 30 years of daily rainfall-runoff data from US catchments ranging in size from 4 to 2,000 km2, with aridity indexes ranging from 0.22 to 5.20, and 12 of the 13 IGPB vegetated land cover classes. Over a 15-year validation period, this effectively "ungauged" model was compared to the Sacramento Soil Moisture Accounting (SAC-SMA) model as well as the NOAA National Water Model reanalysis. Each basin's SAC-SMA was calibrated separately using 15 years of daily data. Across the 531 basins, the out-of-sample LSTM exhibited greater median Nash-Sutcliffe Efficiencies (0.69) than either the calibrated SAC-SMA (0.64) or the National Water Model (0.64). (0.58). This means that there is usually enough information in available catchment attributes data about similarities and differences between catchment-level rainfall-runoff behaviors to generate out-of-sample simulations that are generally more accurate than current models under ideal (i.e., calibrated) conditions. We discovered evidence that adding physical restrictions to the LSTM models improves simulations, which we believe should be the focus of future physics-guided machine learning research.

35

Suryani, Dewi, Mohamad Nur, and Wasis Wasis. "PENGEMBANGAN PROTOTIPE PERANGKAT PEMBELAJARAN FISIKA SMK MODEL INKUIRI TERBIMBING MATERI CERMIN UNTUK MELATIHKAN KETERAMPILAN BERPIKIR KRITIS." JPPS (Jurnal Penelitian Pendidikan Sains) 6, no. 1 (January 31, 2017): 1175. http://dx.doi.org/10.26740/jpps.v6n1.p1175-1183.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This research development aims to determine the quality (validity, practicality, and effectiveness) of Physics Learning Material Using Guided Inquiry Model to Practice Student’s Critical Thinking Skills in subject of Mirror for class XI State Vocational High School 2 Kota Pasuruan. This research was conducted in two phases, namely the development of physics learning materials using the Dick and Carey design, continued with implementation of the learning in the classroom using One Group Pretest-Posttest Design. The result showed that the developing package has a valid, pratical, and effective. Validity showed by the validator assessment of the Teacher’s Book Prototype; Student’s Book Prototype; Student’s Worksheets; Learning outcomes assessment instruments of Affective, Cognitive Product, Process, Critical Thinking Skills and Psycomotor. Practicality seen from the percentage of 100% level of practicality; scores practicality in class XI of Machine Technique is 3.78; activities that support the guided inquiry model are more dominant in learning and the irrelevant activity has decreased in every meeting; students responded positively to the student’s book prototype and process of learning using guided inquiry model that has been developed. Effectiveness seen from individual mastery of cognitive product outcomes of students of class XI Machine Technique is 100%; individual mastery of affective and psycomotor learning outcomes are 100%; all students’s critical thinking skills is increase, 10% of students change the skill from skillfull to very skillfull; 40% of students change the skill from no skillfull to skillfull and 50% change from less skillfull to skillfull. Obstacles encountered in this study are less efficient in guiding students carry out experiments and practice critical thinking skills. The research showed that the physics learning package using guided inquiry model has valid, practical, and effective to practice critical thinking skills.Penelitian pengembangan ini bertujuan untuk mengetahui kualitas (validitas, kepraktisan, dan keefektifan) perangkat pembelajaran fisika menggunakan model inkuiri terbimbing untuk melatihkan keterampilan berpikir kritis pada materi Cermin untuk siswa kelas XI SMK Negeri 2 Kota Pasuruan. Penelitian dilaksanakan dalam dua tahap, yaitu pengembangan perangkat mengikuti rancangan Dick dan Carey, dilanjutkan implementasi perangkat pembelajaran di kelas menggunakan One Group Pretest-Posttest Design. Hasil penelitian menunjukkan bahwa perangkat yang dikembangkan telah valid, praktis, dan efektif. Valid terlihat dari penilaian validator terhadap Model Pembelajaran; Buku Guru; Buku Siswa; LKS; Instrumen Penilaian Hasil Belajar Afektif, Kognitif Produk, Proses, Keterampilan Berpikir Kritis dan Psikomotor. Praktis terlihat dari persentase keterlaksanaan tahapan pembelajaran sebesar 100%, dengan skor keterlaksanaan pembelajaran di kelas XI Teknik Permesinan sebesar 3,78; aktivitas yang mendukung model inkuiri terbimbing lebih dominan dalam pembelajaran dan aktivitas tidak relevan mengalami penurunan setiap pertemuan; siswa memberikan respon positif terhadap prototipe buku siswa dan proses pembelajaran menggunakan model inkuiri terbimbing yang telah dikembangkan. Efektif terlihat dari ketuntasan individual hasil belajar kognitif produk siswa kelas XI Teknik Pemesinan sebesar 100%; ketuntasan hasil belajar afektif dan psikomotor siswa sebesar 100%; semua siswa mengalami peningkatan keterampilan berpikir kritis, dari seluruh siswa 3% mengalami perubahan dari terampil menjadi sangat terampil; 50% dari tidak terampil menjadi terampil; 47% dari kurang terampil menjadi terampil. Kendala yang dihadapi dalam penelitian ini adalah kurang efisien waktu dalam membimbing siswa melaksanakan eksperimen dan melatihkan keterampilan berpikir kritis. Hasil penelitian menunjukkan bahwa perangkat pembelajaran fisika menggunakan model inkuiri terbimbing telah valid, praktis, dan efektif untuk melatihkan keterampilan berpikir kritis.

36

Biswas, Reetam, Mrinal K. Sen, Vishal Das, and Tapan Mukerji. "Prestack and poststack inversion using a physics-guided convolutional neural network." Interpretation 7, no. 3 (August 1, 2019): SE161—SE174. http://dx.doi.org/10.1190/int-2018-0236.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

An inversion algorithm is commonly used to estimate the elastic properties, such as P-wave velocity ([Formula: see text]), S-wave velocity ([Formula: see text]), and density ([Formula: see text]) of the earth’s subsurface. Generally, the seismic inversion problem is solved using one of the traditional optimization algorithms. These algorithms start with a given model and update the model at each iteration, following a physics-based rule. The algorithm is applied at each common depth point (CDP) independently to estimate the elastic parameters. Here, we have developed a technique using the convolutional neural network (CNN) to solve the same problem. We perform two critical steps to take advantage of the generalization capability of CNN and the physics to generate synthetic data for a meaningful representation of the subsurface. First, rather than using CNN as in a classification type of problem, which is the standard approach, we modified the CNN to solve a regression problem to estimate the elastic properties. Second, again unlike the conventional CNN, which is trained by supervised learning with predetermined label (elastic parameter) values, we use the physics of our forward problem to train the weights. There are two parts of the network: The first is the convolution network, which takes the input as seismic data to predict the elastic parameters, which is the desired intermediate result. In the second part of the network, we use wave-propagation physics and we use the output of the CNN to generate the predicted seismic data for comparison with the actual data and calculation of the error. This error between the true and predicted seismograms is then used to calculate gradients, and update the weights in the CNN. After the network is trained, only the first part of the network can be used to estimate elastic properties at remaining CDPs directly. We determine the application of physics-guided CNN on prestack and poststack inversion problems. To explain how the algorithm works, we examine it using a conventional CNN workflow without any physics guidance. We first implement the algorithm on a synthetic data set for prestack and poststack data and then apply it to a real data set from the Cana field. In all the training examples, we use a maximum of 20% of data. Our approach offers a distinct advantage over a conventional machine-learning approach in that we circumvent the need for labeled data sets for training.

37

Xie, Yazhou, Majid Ebad Sichani, Jamie E. Padgett, and Reginald DesRoches. "The promise of implementing machine learning in earthquake engineering: A state-of-the-art review." Earthquake Spectra 36, no. 4 (June 3, 2020): 1769–801. http://dx.doi.org/10.1177/8755293020919419.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

38

Zhang, Hongliang, Kristopher A. Innanen, and David W. Eaton. "Inversion for Shear-Tensile Focal Mechanisms Using an Unsupervised Physics-Guided Neural Network." Seismological Research Letters 92, no. 4 (March 24, 2021): 2282–94. http://dx.doi.org/10.1785/0220200420.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract We present a novel physics-guided neural network to estimate shear-tensile focal mechanisms for microearthquakes using displacement amplitudes of direct P waves. Compared with conventional data-driven fully connected (FC) neural networks, our physics-guided neural network is implemented in an unsupervised fashion and avoids the use of training data, which may be incomplete or unavailable. We incorporate three FC layers and a scaling and shifting layer to estimate shear-tensile focal mechanisms for multiple events. Then, a forward-modeling layer, which generates synthetic amplitude data based on the source mechanisms emerging from the previous layer, is added. The neural network weights are iteratively updated to minimize the mean squared error between observed and modeled normalized P-wave amplitudes. We apply this machine-learning approach to a set of 530 induced events recorded during hydraulic-fracture simulation of Duvernay Shale west of Fox Creek, Alberta, yielding results that are consistent with previously reported source mechanisms for the same dataset. A distinct cluster characterized by more complex mechanisms exhibits relatively large Kagan angles (5°–25°) compared with the previously reported best double-couple solutions, mainly due to model simplification of the shear-tensile focal mechanism. Uncertainty tests demonstrate the robustness of the inversion results and high tolerance of our neural network to errors in event locations, the velocity model, and P-wave amplitudes. Compared with a single-event grid-search algorithm to estimate shear-tensile focal mechanisms, the proposed neural network approach exhibits significantly higher computational efficiency.

39

Gopalakrishnan Meena, Muralikrishnan, Amir K. Ziabari, Singanallur V. Venkatakrishnan, Isaac R. Lyngaas, Matthew R. Norman, Balint Joo, Thomas L. Beck, Charles A. Bouman, Anuj J. Kapadia, and Xiao Wang. "Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications." Electronic Imaging 35, no. 11 (January 16, 2023): 228–1. http://dx.doi.org/10.2352/ei.2023.35.11.hpci-228.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Naser, M. Z. "Mapping functions: A physics-guided, data-driven and algorithm-agnostic machine learning approach to discover causal and descriptive expressions of engineering phenomena." Measurement 185 (November 2021): 110098. http://dx.doi.org/10.1016/j.measurement.2021.110098.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Seyyedi, Azra, Mahdi Bohlouli, and SeyedEhsan Nedaaee Oskoee. "Machine Learning and Physics: A Survey of Integrated Models." ACM Computing Surveys, August 3, 2023. http://dx.doi.org/10.1145/3611383.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Predictive modeling of various systems around the world is extremely essential from the physics and engineering perspectives. The recognition of different systems and the capacity to predict their future behavior can lead to numerous significant applications. For the most part, physics is frequently used to model different systems. Using physical modeling can also very well help the resolution of complexity and achieve superior performance with the emerging field of novel artificial intelligence and the challenges associated with it. Physical modeling provides data and knowledge that offer meaningful and complementary understanding about the system. So, by using enriched data and training phases, the overall general integrated model achieves enhanced accuracy. The effectiveness of hybrid physics-guided or machine learning-guided models has been validated by experimental results of diverse use cases. Increased accuracy, interpretability, and transparency are the results of such hybrid models. In this paper, we provide a detailed overview of how machine learning and physics can be integrated into an interactive approach. Regarding this, we propose a classification of possible interactions between physical modeling and machine learning techniques. Our classification includes three types of approaches: (1) physics-guided machine learning (2) machine learning-guided physics, and (3) mutually-guided physics and ML. We studied the models and specifications for each of these three approaches in-depth for this survey.

42

Garpelli, Lucas Nogueira, Diogo Stuani Alves, Katia Lucchesi Cavalca, and Helio Fiori de Castro. "Physics-guided neural networks applied in rotor unbalance problems." Structural Health Monitoring, April 10, 2023, 147592172311630. http://dx.doi.org/10.1177/14759217231163081.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Rotary systems are extremely important for the development of industrial production due to the large amount of work performed by these machines. However, rotating machines are prone to unbalance faults, which can reduce efficiency and, in the worst-case scenario, lead to catastrophic failure. Since artificial neural networks (ANNs) are very efficient at recognizing complex patterns, they are a useful tool to help diagnose and prevent rotor unbalance faults. Physics-Guided Machine Learning (PGML) is a class of machine learning algorithm that uses physical laws in its structure. In this paper, a method for unbalance fault identification using PGML is proposed, more specifically ANNs as machine learning—Physics-Guided Neural Networks (PGNN) is used. The first step adopts a standard ANN to locate the nodal position of the experimental fault. Afterwards, the PGNN is performed to quantify the unbalance magnitude and phase angle. Also, a comparison between the performance of the standard ANN and PGNN is accomplished. As input, the networks use simulation data of a rotor supported by hydrodynamic bearings modeled through the finite element method (FEM) and Reynolds’ equation. The results showed that the PGNN has smaller errors and better performance than the standard ANN. In addition, a small increase in the number of neurons improves the results of both networks.

43

Zhao, Luanxiao, Jingyu Liu, Minghui Xu, Zhenyu Zhu, Yuanyuan Chen, and Jianhua Geng. "Rock Physics guided machine learning for shear sonic log prediction." GEOPHYSICS, October 11, 2023, 1–71. http://dx.doi.org/10.1190/geo2023-0152.1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Shear wave velocity (Vs) is a vital parameter for various petrophysical, geophysical, and geomechanical applications in subsurface characterization. However, obtaining shear sonic log is often challenging since it often costs extra budget and time to acquire. Conventional methods for predicting Vs often rely on empirical relationships and rock physics models. However, these models often fall short in accuracy due to their inability to account for the complex nonlinear factors affecting the relationship between Vs and other parameters. We propose a physics-guided machine learning approach to predict shear sonic log using the various physical parameters (e.g. natural gamma ray, P-wave velocity, density, resistivity) that can be routinely obtained from standard logging suites. Three types of rock physical constraints including the mudrock line, empirical P- and S- wave velocity relationship and multi-parameter regression from the logging data, are combined with three physical guidance strategies including constructing physics-guided pseudo labels, physics-guided loss function and transfer learning, to blind test four wells based on one training well in a clastic reservoir. Compared to pure supervised ML, all the model that incorporates physical constraints significantly improves prediction accuracy and generalization performance, demonstrating the importance of incorporating first-order physical laws into data-driven network training. The multi-parameter regression relationship combined with the strategy of constructing physics-guided pseudo labels gives the best prediction performance, with the average root mean square error (RMSE) of the blind test dropping by 47%.

44

Braiek, Houssem Ben, Thomas Reid, and Foutse Khomh. "Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation." IEEE Transactions on Reliability, 2022, 1–15. http://dx.doi.org/10.1109/tr.2022.3196272.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Chen, Jie, and Yongming Liu. "Probabilistic physics-guided machine learning for fatigue data analysis." Expert Systems with Applications, November 2020, 114316. http://dx.doi.org/10.1016/j.eswa.2020.114316.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Nakazawa, Ryota, Yuki Minamoto, Nakamasa Inoue, and Mamoru Tanahashi. "Species reaction rate modelling based on physics-guided machine learning." Combustion and Flame, August 2021, 111696. http://dx.doi.org/10.1016/j.combustflame.2021.111696.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Greis, Noel P., Monica L. Nogueira, Sambit Bhattacharya, Catherine Spooner, and Tony Schmitz. "Stability modeling for chatter avoidance in self-aware machining: an application of physics-guided machine learning." Journal of Intelligent Manufacturing, November 9, 2022. http://dx.doi.org/10.1007/s10845-022-01999-w.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

AbstractPhysics-guided machine learning (PGML) offers a new approach to stability modeling during machining that leverages experimental data generated during the machining process while incorporating decades of theoretical process modeling efforts. This approach addresses specific limitations of machine learning models and physics-based models individually. Data-driven machine learning models are typically black box models that do not provide deep insight into the underlying physics and do not reflect physical constraints for the modeled system, sometimes yielding solutions that violate physical laws or operational constraints. In addition, acquiring the large amounts of manufacturing data needed for machine learning modeling can be costly. On the other hand, many physical processes are not completely understood by domain experts and have a high degree of uncertainty. Physics-based models must make simplifying assumptions that can compromise prediction accuracy. This research explores whether data generated by an uncertain physics-based milling stability model that is used to train a physics-guided machine learning stability model, and then updated with measured data, domain knowledge, and theory-based knowledge provides a useful approximation to the unknown true stability model for a specific set of factory operating conditions. Four novel strategies for updating the machine learning model with experimental data are explored. These updating strategies differ in their assumptions about and implementation of the type of physics-based knowledge included in the PGML model. Using a simulation experiment, these strategies achieve useful approximations of the underlying true stability model while reducing the number of experimental measurements required for model update.

48

Guo, Lulu, Jin Ye, and Bowen Yang. "Cyber-Attack Detection for Electric Vehicles Using Physics-Guided Machine Learning." IEEE Transactions on Transportation Electrification, 2020, 1. http://dx.doi.org/10.1109/tte.2020.3044524.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Abu-Mualla, Mohammad, and Jida Huang. "Inverse Design of 3D Cellular Materials with Physics-Guided Machine Learning." Materials & Design, July 2023, 112103. http://dx.doi.org/10.1016/j.matdes.2023.112103.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Chen, Shengyu, Nasrin Kalanat, Yiqun Xie, Sheng Li, Jacob A. Zwart, Jeffrey M. Sadler, Alison P. Appling, Samantha K. Oliver, Jordan S. Read, and Xiaowei Jia. "Physics-guided machine learning from simulated data with different physical parameters." Knowledge and Information Systems, March 31, 2023. http://dx.doi.org/10.1007/s10115-023-01864-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Physics-guided Machine Learning'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles