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What machine learning can do for computational solid mechanics
[article]
2021
arXiv
pre-print
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Here, we dare to give a non-exhaustive overview of potential avenues for machine learning in the numerical modeling of solids and structures and offer our (subjective) perspective on what is yet to come ...
Machine learning has found its way into almost every area of science and engineering, and we are only at the beginning of its exploration across fields. ...
are accelerated by big data and advances in machine learning (ML) strategies. ...
arXiv:2109.08419v1
fatcat:epx4zadlwncm7kveff76xxqip4
Topology optimization of 2D structures with nonlinearities using deep learning
[article]
2020
arXiv
pre-print
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With the availability of cloud computing, including high-performance computing, machine learning, and simulation, searching for optimal nonlinear structures is now within reach. ...
The field of optimal design of linear elastic structures has seen many exciting successes that resulted in new architected materials and structural designs. ...
Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. ...
arXiv:2002.01896v4
fatcat:66egnxngrraf5grakwwcxivyde
Robust Longitudinal Control for Vehicular Autonomous Platoons Using Deep Reinforcement Learning
[article]
2022
arXiv
pre-print
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In the last few years, researchers have applied machine learning strategies in the context of vehicular platoons to increase the safety and efficiency of cooperative transportation. ...
Therefore, in this paper, we propose an approach to generalize the training process of a vehicular platoon, such that the acceleration command of each agent becomes independent of the network topology. ...
FIGURE 3 3 FIGURE 3 Nonlinear control structure for the case of the training environment.
FIGURE 4 4 FIGURE 4 Proposed nonlinear control structure for the case of the training environment. ...
arXiv:2206.01175v1
fatcat:oiewcxvhsnh4xl4bznwi76kkke
novel approach of drug repurposing in immuno-oncology therapeutic agents using machine learning algorithm
2022
International Journal of Health Sciences
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Today Machine learning algorithms based automated systems are gaining attention for creating new device applications in the field of artificial intelligence. [2] The present study used machine learning ...
Classification linear and nonlinear algorithms such as logistic regression,Support Vector Machine (Kernel) and Random Forest were used to build the computational models. ...
forest and SVM with nonlinear kernels) machine learning classifier models with a large number of descriptors. ...
doi:10.53730/ijhs.v6ns2.7523
fatcat:ymlpxv6wtrc7ddb6zl4b545jf4
Unit module-based convergence acceleration for topology optimization using the spatiotemporal deep neural network
2021
IEEE Access
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This study proposes a unit module-based acceleration method for 2-D topology optimization. For the purpose, the first-stage topology optimization is performed until the predefined iteration. ...
Then, in the second-stage topology optimization, a combined near-optimal design of a whole design domain is used as an initial design to determine the optimized design in a more efficient way. ...
ACKNOWLEDGMENT Replication of results: Compiled codes and relevant materials are provided through a repository. (Younghwan Joo and Yonggyun Yu are co-first authors.) ...
doi:10.1109/access.2021.3125014
fatcat:f4h2pb62gzgu5pmrd7amzsaa6m
Recent Advances on the Design Automation for Performance-Optimized Fiber Reinforced Polymer Composite Components
2020
Journal of Composites Science
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They include studies on microstructure-based material design, applications of machine learning models in stress analysis, and topology optimization of fiber-reinforced polymer composites. ...
In this review, the applications of machine learning methods in various aspects of structural component design are discussed. ...
This work was carried out using resources from School of Mechanical and Aerospace Engineering, Nanyang Technological University.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/jcs4020061
fatcat:5x2767kdmbayxoapyffzq6eqry
Inverse Design of Materials by Machine Learning
2022
Materials
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With the development of physics, statistics, computer science, etc., machine learning offers the opportunity to systematically find new materials. ...
Especially by inverse design based on machine learning, one can make use of the existing knowledge without attempting mathematical inversion of the relevant integrated differential equation of the electronic ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/ma15051811
pmid:35269043
pmcid:PMC8911677
fatcat:l4bgwqnn5vhwhfcboccdgnpg2a
LKLR: A local tangent space-alignment kernel least-squares regression algorithm
2019
Tsinghua Science and Technology
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To achieve this, we use a nonlinear manifold learning algorithm to transform the local topological structure from the feature space to the label space. ...
In the fields of machine learning and data mining, label learning is a nascent area of research, and within this paradigm, there is much room for improving multi-label manifold learning algorithms for ...
On the basis of nonlinear manifold learning methods such as the local tangent space alignment learning algorithm, we transform the local topological structure from the feature space to the label space ...
doi:10.26599/tst.2018.9010120
fatcat:lm7yiaw5dnfixfxbbtogsrbo6e
Geometry and Topology Optimization of Switched Reluctance Machines: A Review
2022
IEEE Access
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We discuss how these techniques are applied to optimize the geometries and topologies of SRMs to enhance machine performance. ...
On the other hand, the material distribution in a particular design space within the machine domain may be optimized using topology optimization. ...
There are two feasible deep learning techniques for topology optimization computational acceleration: online and offline techniques [209] , [213] . ...
doi:10.1109/access.2022.3140440
fatcat:76eqna2mrnezboalh57f4it3qq
Machine learning for composite materials
2019
MRS Communications
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Machine learning (ML) has been perceived as a promising tool for the design and discovery of novel materials for a broad range of applications. ...
An overview of how different types of ML algorithms can be applied to accelerate composite research is presented. ...
Acknowledgments The authors acknowledge support from the Regents of the University of California, Berkeley. ...
doi:10.1557/mrc.2019.32
fatcat:rowg6n2wtfgzpdvunokemj356q
Intelligent mobile information system for underground
2017
Electronics and Control Systems
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Ways to improve ease of subway passengers is considered. Environmental parameters for prediction problems are studied. Choice of machine learning methods is made. ...
The accuracy of the prediction is estimated. ...
methods of statistical analysis and machine learning. ...
doi:10.18372/1990-5548.51.11699
fatcat:wsyi63i6n5amnnem54eyba7sem
The GRD chip: genetic reconfiguration of DSPs for neural network processing
1999
IEEE transactions on computers
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Both the topology and the hidden layer node functions of a neural network mapped on the GRD chips are dynamically reconfigured using a genetic algorithm (GA). ...
Thus, the most desirable network topology and choice of node functions (e.g., Gaussian or sigmoid function) for a given application can be determined adaptively. ...
A system built with the GRD chips can dynamically reconfigure its hardware structure to be tailored to the optimal topology without a host machine. ...
doi:10.1109/12.773799
fatcat:43tz4h2bpzhefcsxrhjrvpxsdu
Intelligent on-demand design of phononic metamaterials
2022
Nanophotonics
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Machine learning provides a powerful means of achieving an efficient and accurate design process by exploring nonlinear physical patterns in high-dimensional space, based on data sets of candidate structures ...
In this review, we summarize the recent works on the combination of phononic metamaterials and machine learning. We provide an overview of machine learning on structural design. ...
Figure 1 : 1 Figure1: Diagrammatic sketch of machine learning for properties characterization and structural design of artificial materials. ...
doi:10.1515/nanoph-2021-0639
fatcat:tonxwxrztvhudmgztlx2h7kwre
Fiber laser development enabled by machine learning: review and prospect
2022
PhotoniX
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This paper highlights recent attractive research that adopted machine learning in the fiber laser field, including design and manipulation for on-demand laser output, prediction and control of nonlinear ...
AbstractIn recent years, machine learning, especially various deep neural networks, as an emerging technique for data analysis and processing, has brought novel insights into the development of fiber lasers ...
Concept The field of machine learning and optimization are intertwined. Most machine learning problems can transform into optimization ones in the end. ...
doi:10.1186/s43074-022-00055-3
fatcat:owxt6uutwbhjjf7sct5mqhrkbi
Prediction of IPM Machine Torque Characteristics Using Deep Learning Based on Magnetic Field Distribution
2022
IEEE Access
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Topology optimization can be effectively accelerated because the number of analyses by the finite element method can be reduced using the proposed method. ...
Furthermore, the DNN learned by the proposed method is applied to the topology optimization algorithm. ...
Therefore, it is expected to accelerate the learning process. ...
doi:10.1109/access.2022.3179835
fatcat:jkzmo3i7areaddwswq7ejopfk4
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