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What machine learning can do for computational solid mechanics [article]

Siddhant Kumar, Dennis M. Kochmann
2021 arXiv   pre-print
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]

Diab W. Abueidda, Seid Koric, Nahil A. Sobh
2020 arXiv   pre-print
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]

Armando Alves Neto, Leonardo Amaral Mozelli
2022 arXiv   pre-print
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

Deepak Srivastava, Pramod Kumar, Sunil Ghildiyal
2022 International Journal of Health Sciences  
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

Younghwan Joo, Yonggyun Yu, In Gwun Jang
2021 IEEE Access  
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

Yi Di Boon, Sunil Chandrakant Joshi, Somen Kumar Bhudolia, Goram Gohel
2020 Journal of Composites Science  
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

Jia Wang, Yingxue Wang, Yanan Chen
2022 Materials  
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

Chao Tan, Genlin Ji
2019 Tsinghua Science and Technology  
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

Mohamed Abdalmagid, Ehab Sayed, Mohamed Bakr, Ali Emadi
2022 IEEE Access  
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

Chun-Teh Chen, Grace X. Gu
2019 MRS Communications  
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

O. I. Chumachenko, I. V. Roshinsky
2017 Electronics and Control Systems  
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

M. Murakawa, S. Yoshizawa, I. Kajitani, X. Yao, N. Kajihara, M. Iwata, T. Higuchi
1999 IEEE transactions on computers  
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

Yabin Jin, Liangshu He, Zhihui Wen, Bohayra Mortazavi, Hongwei Guo, Daniel Torrent, Bahram Djafari-Rouhani, Timon Rabczuk, Xiaoying Zhuang, Yan Li
2022 Nanophotonics  
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

Min Jiang, Hanshuo Wu, Yi An, Tianyue Hou, Qi Chang, Liangjin Huang, Jun Li, Rongtao Su, Pu Zhou
2022 PhotoniX  
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

Hidenori Sasaki, Yuki Hidaka, Hajime Igarashi
2022 IEEE Access  
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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