Search Results - Data-driven computational cost reduction

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  1. 1

    Neural network design for data-driven prediction of target geometry for an aerodynamic inverse design algorithm by Shirvani, Ahmad, Nili-Ahmadabadi, Mahdi, Ha, Man Yeong

    ISSN: 1738-494X, 1976-3824
    Published: Seoul Korean Society of Mechanical Engineers 01.08.2024
    “…) and long-short term memory (LSTM) network to predict the target geometry with early generated data of the design algorithm to reduce its computational cost…”
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    Journal Article
  2. 2

    Data-driven model reduction for the Bayesian solution of inverse problems by Cui, Tiangang, Marzouk, Youssef M., Willcox, Karen E.

    ISSN: 0029-5981, 1097-0207
    Published: Bognor Regis Blackwell Publishing Ltd 04.05.2015
    “…) methods for posterior sampling. This paper proposes a datadriven projection‐based model reduction technique to reduce this computational cost…”
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    Journal Article
  3. 3

    E-URES: Efficient User-Centric Residual-Echo Suppression Framework with a Data-Driven Approach to Reducing Computational Costs by Ivry, Amir, Cohen, Israel

    ISSN: 2835-3439
    Published: IEEE 09.09.2024
    “… This paper introduces an efficient URES (E-URES) framework, which reduces computational costs in the final stage of the URES pipeline by minimizing the number of AECMOS computations…”
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    Conference Proceeding
  4. 4

    Data driven surrogate modeling of horn antennas for optimal determination of radiation pattern and size using deep learning by Piltan, Onur Can, Kizilay, Ahmet, Belen, Mehmet A., Mahouti, Peyman

    ISSN: 0895-2477, 1098-2760
    Published: New York Wiley Subscription Services, Inc 01.01.2024
    Published in Microwave and optical technology letters (01.01.2024)
    “…Horn antenna designs are favored in many applications where ultra‐wide‐band operation range alongside of a high‐performance radiation pattern characteristics…”
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    Journal Article
  5. 5

    Data-driven model order reduction with proper symplectic decomposition for flexible multibody system by Peng, Haijun, Song, Ningning, Kan, Ziyun

    ISSN: 0924-090X, 1573-269X
    Published: Dordrecht Springer Netherlands 01.01.2022
    Published in Nonlinear dynamics (01.01.2022)
    “… In order to save the computational cost for simulating flexible multibody system, a novel model order reduction strategy based on the idea of data-driven model is proposed…”
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    Journal Article
  6. 6

    Surrogate Model Based on Data-Driven Model Reduction for Inelastic Behavior of Composite Microstructure by Kim, Hyejin, Jeong, Inho, Cho, Haeseong, Cho, Maenghyo

    ISSN: 2093-274X, 2093-2480
    Published: Seoul The Korean Society for Aeronautical & Space Sciences (KSAS) 01.07.2023
    “… However, a significant computational cost may be incurred owing to the iterative procedure when considering the inelastic behavior of composite materials…”
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    Journal Article
  7. 7

    Encoder–Decoder Convolutional Neural Networks for Flow Modeling in Unsaturated Porous Media: Forward and Inverse Approaches by Hajizadeh Javaran, Mohammad Reza, Rajabi, Mohammad Mahdi, Kamali, Nima, Fahs, Marwan, Belfort, Benjamin

    ISSN: 2073-4441, 2073-4441
    Published: Basel MDPI AG 01.08.2023
    Published in Water (Basel) (01.08.2023)
    “… Data-driven modeling offers a faster and more efficient way to estimate soil moisture dynamics, significantly reducing computational costs…”
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    Journal Article
  8. 8

    A hybrid data-driven model order reduction strategy for flexible multibody systems considering impact and friction by Song, Ningning, Peng, Haijun, Kan, Ziyun

    ISSN: 0094-114X
    Published: Elsevier Ltd 01.03.2022
    Published in Mechanism and machine theory (01.03.2022)
    “… In this work, the nonsmooth contact method (NSCM) for FMBSs is briefly introduced. Moreover, in order to save the computational costs of a contact FMBS, a novel hybrid model order reduction (MOR…”
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    Journal Article
  9. 9

    Data-driven models for crashworthiness optimisation: intrusive and non-intrusive model order reduction techniques by Czech, Catharina, Lesjak, Mathias, Bach, Christopher, Duddeck, Fabian

    ISSN: 1615-147X, 1615-1488
    Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2022
    “… Therefore, data-driven Model Order Reduction (MOR) aims at generating low-fidelity models that approximate the solution while strongly reducing the computational cost…”
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    Journal Article
  10. 10

    Data-driven modelling of fully nonlinear wave loads on offshore wind-turbine monopiles at prototype scale by Tan, Weikai, Ren, Pu, Cao, Deping, Liang, Hui, Chen, Hao

    ISSN: 0951-8339
    Published: Elsevier Ltd 15.04.2025
    Published in Marine structures (15.04.2025)
    “… These approaches offer substantial reductions in computational cost while maintaining reasonable predictive accuracy for high-order wave loadings under a range…”
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    Journal Article
  11. 11

    Uncertain dynamics characteristic forecasting in composite plates with multi-defects of electric aircraft via physics-augmented meta-learning by Xu, Duo, Zang, Jian, Song, Xu-Yuan, Zhang, Zhen, Zhang, Ye-Wei, Chen, Li-Qun

    ISSN: 1270-9638
    Published: Elsevier Masson SAS 01.09.2025
    Published in Aerospace science and technology (01.09.2025)
    “…•The effectiveness of the PMF has been verified through comparison with physics-data driven models…”
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    Journal Article
  12. 12

    A Hybrid Approach for Process Monitoring: Improving Data-Driven Methodologies With Dataset Size Reduction and Interval-Valued Representation by Dhibi, Khaled, Fezai, Radhia, Mansouri, Majdi, Kouadri, Abdelmalek, Harkat, Mohamed-Faouzi, Bouzara, Kais, Nounou, Hazem, Nounou, Mohamed

    ISSN: 1530-437X, 1558-1748
    Published: New York IEEE 01.09.2020
    Published in IEEE sensors journal (01.09.2020)
    “…Kernel principal component analysis (KPCA) is a well-established data-driven process modeling and monitoring framework that has long been praised for its performances…”
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    Journal Article
  13. 13

    Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load by Wang, Chu, Dou, Manfeng, Li, Zhongliang, Outbib, Rachid, Zhao, Dongdong, Zuo, Jian, Wang, Yuanlin, Liang, Bin, Wang, Peng

    ISSN: 0951-8320, 1879-0836
    Published: Elsevier Ltd 01.05.2023
    Published in Reliability engineering & system safety (01.05.2023)
    “…). For the prognostics of PEMFC operating under dynamic load, the challenges come from extracting degradation features, improving prediction accuracy, expanding the prognostics horizon, and reducing computational cost…”
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    Journal Article
  14. 14

    Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process by McQuarrie, Shane A., Huang, Cheng, Willcox, Karen E.

    ISSN: 0303-6758, 1175-8899, 1175-8899
    Published: Wellington Taylor & Francis 01.06.2021
    “…This paper derives predictive reduced-order models for rocket engine combustion dynamics via Operator Inference, a scientific machine learning approach that blends data-driven learning with physics-based modelling…”
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    Journal Article
  15. 15

    Using proper orthogonal decomposition for data-driven model error correction for heat conduction problems with uncertain boundary conditions by Hellberg, Karl G., Heyns, P. Stephan, Wannenburg, Johann

    ISSN: 0307-904X
    Published: Elsevier Inc 01.10.2025
    Published in Applied mathematical modelling (01.10.2025)
    “… Reduced order modelling, often involving dimensionality reduction, together with data-driven model error correction, can make it possible to achieve both objectives…”
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    Journal Article
  16. 16

    Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism by Lin, Kevin K., Lu, Fei

    ISSN: 0021-9991, 1090-2716
    Published: Cambridge Elsevier Inc 01.01.2021
    Published in Journal of computational physics (01.01.2021)
    “…•Data-driven model reduction for deterministic and random dynamical systems.•A precise formulation of data-driven modeling based on discrete-time Mori-Zwanzig formalism…”
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    Journal Article
  17. 17

    Data-driven uncertainty quantification in computational human head models by Upadhyay, Kshitiz, Giovanis, Dimitris G., Alshareef, Ahmed, Knutsen, Andrew K., Johnson, Curtis L., Carass, Aaron, Bayly, Philip V., Shields, Michael D., Ramesh, K.T.

    ISSN: 0045-7825, 1879-2138
    Published: Amsterdam Elsevier B.V 01.08.2022
    “… Modern biofidelic head model simulations are associated with very high computational cost and high-dimensional inputs and outputs, which limits the applicability of traditional UQ methods…”
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    Journal Article
  18. 18

    Low-cost data-driven modelling of microwave components using domain confinement and PCA-based dimensionality reduction by Koziel, Slawomir, Pietrenko-Dabrowska, Anna

    ISSN: 1751-8725, 1751-8733
    Published: The Institution of Engineering and Technology 28.10.2020
    Published in IET microwaves, antennas & propagation (28.10.2020)
    “…Fast data-driven surrogate models can be employed as replacements of computationally demanding full-wave electromagnetic simulations to facilitate the microwave design procedures…”
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    Journal Article
  19. 19

    Data-driven molecular modeling with the generalized Langevin equation by Grogan, Francesca, Lei, Huan, Li, Xiantao, Baker, Nathan A.

    ISSN: 0021-9991, 1090-2716
    Published: Cambridge Elsevier Inc 01.10.2020
    Published in Journal of computational physics (01.10.2020)
    “…•Data-driven GLE approximation balances computational cost and accuracy.•Accuracy tunable by adjusting order of memory kernel approximation…”
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    Journal Article
  20. 20

    Hybridization of data-driven threshold algorithm with fuzzy particle swarm optimization technique for gene selection in microarray data by Adebayo, Paul Olujide, Jimoh, Rasheed Gbenga, Yahya, Waheed Babatunde

    ISSN: 2468-2276, 2468-2276
    Published: Elsevier B.V 01.03.2024
    Published in Scientific African (01.03.2024)
    “…) optimisation capabilities. The proposed hybrid method serves multiple objectives, including minimizing the number of selected genes for model training, reducing computational costs, assessing each gene's contribution…”
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    Journal Article