Search Results - physics-embedded algorithm structure~

  • Showing 1 - 15 results of 15
Refine Results
  1. 1

    Physics-informed machine learning in prognostics and health management: State of the art and challenges by DENG, Weikun, NGUYEN, Khanh T.P., MEDJAHER, Kamal, GOGU, Christian, MORIO, Jérôme

    ISSN: 0307-904X, 1872-8480
    Published: Elsevier Inc 01.12.2023
    Published in Applied mathematical modelling (01.12.2023)
    “…•Systematic bibliometric analysis of PIML in PHM.•Novel perspectives for PIML from the “Informed knowledge forms” and “Informed methods”.•Taxonomy of PIML…”
    Get full text
    Journal Article
  2. 2

    A review of physics-informed machine learning for building energy modeling by Ma, Zhihao, Jiang, Gang, Hu, Yuqing, Chen, Jianli

    ISSN: 0306-2619
    Published: Elsevier Ltd 01.03.2025
    Published in Applied energy (01.03.2025)
    “…) algorithms in recent years, several challenges remain to apply these data-driven approaches in BEM, including the necessity of obtaining sufficient and high-quality training data in algorithm…”
    Get full text
    Journal Article
  3. 3

    Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface by Wu, Hao, Greer, Sarah Y., O’Malley, Daniel

    ISSN: 2045-2322, 2045-2322
    Published: London Nature Publishing Group UK 13.01.2023
    Published in Scientific reports (13.01.2023)
    “… We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g…”
    Get full text
    Journal Article
  4. 4

    Layered Soil Remote Sensing With Multichannel Passive Microwave Observations Using a Physics-Embedded Artificial Intelligence Framework: A Theoretical Study by Bai, Xuyang, Tan, Shurun

    ISSN: 0196-2892, 1558-0644
    Published: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
    “… using multichannel passive microwave observations. To enhance the inversion efficiency and accuracy, a novel physics-embedded artificial neural network (P-ANN…”
    Get full text
    Journal Article
  5. 5

    PhysicsEmbedded Machine Learning for Fatigue Cumulative Damage Prediction by Gao, Zhiyuan, Jiang, Xiaomo, Guo, Yifan, Cui, Mingqing, Wang, Shengbo

    ISSN: 8756-758X, 1460-2695
    Published: Oxford Wiley Subscription Services, Inc 01.10.2025
    “… This study proposes an innovative physicsembedded machine learning (ML) framework to enhance residual fatigue damage prediction by integrating the Manson–Halford (MH…”
    Get full text
    Journal Article
  6. 6

    Physics-embedded deep learning inversion for transient electromagnetic method survey data by Li, Ruiyou, Zhang, Yong, Ju, Jiayi, Liu, Rongqiang

    ISSN: 0098-3004
    Published: Elsevier Ltd 01.10.2025
    Published in Computers & geosciences (01.10.2025)
    “…The transient electromagnetic method (TEM) is a widely used geophysical technique for investigating complex geological conditions. Deep learning (DL) provides…”
    Get full text
    Journal Article
  7. 7

    Layered Soil Remote Sensing with Multi-Channel Passive Microwave Observations Using A Physics Embedded Artificial Intelligence Framework: A Theoretical Study by Bai, Xuyang, Tan, Shurun

    ISSN: 0196-2892
    Published: IEEE 25.10.2023
    “… using multi-channel passive microwave observations. To enhance the inversion efficiency and accuracy, a novel Physics-Embedded Artificial Neural Network (P-ANN…”
    Get full text
    Journal Article
  8. 8

    A physics-embedded deep-learning framework for efficient multi-fidelity modeling applied to guided wave based structural health monitoring by Nerlikar, Vivek, Miorelli, Roberto, Recoquillay, Arnaud, d’Almeida, Oscar

    ISSN: 0041-624X, 1874-9968, 1874-9968
    Published: Netherlands Elsevier B.V 01.07.2024
    Published in Ultrasonics (01.07.2024)
    “…Health monitoring of structures using ultrasonic guided waves is an evolving technology with potential applications in monitoring pipelines, civil bridges, and aircraft components…”
    Get full text
    Journal Article
  9. 9

    Data-Driven Perovskite Design via High-Throughput Simulation and Machine Learning by Wang, Yidi, Sun, Dan, Zhao, Bei, Zhu, Tianyu, Liu, Chengcheng, Xu, Zixuan, Zhou, Tianhang, Xu, Chunming

    ISSN: 2227-9717, 2227-9717
    Published: Basel MDPI AG 01.10.2025
    Published in Processes (01.10.2025)
    “…) in accelerating perovskite discovery. By harnessing existing experimental datasets and high-throughput computational results, ML models elucidate structure-property relationships and predict performance metrics for solar cells, (photo…”
    Get full text
    Journal Article
  10. 10

    A Planar Array Synthesis Method Based on Deep Learning and Radiation Pattern Superposition Method by Huang, Jianming, Liu, Rui, Zhang, Naibo, Cui, Yansong, Ren, Weizheng, Guo, Qiuquan, Du, Yanjun, Zhao, Jiayu

    ISSN: 0018-926X, 1558-2221
    Published: New York IEEE 01.08.2025
    “…A dual-branched convolutional neural network (CNN) integrated with hybrid training criteria is proposed for real-time multibeam synthesis in planar uniform…”
    Get full text
    Journal Article
  11. 11

    Rings: An efficient Java/Scala library for polynomial rings by Poslavsky, Stanislav

    ISSN: 0010-4655, 1879-2944
    Published: Elsevier B.V 01.02.2019
    Published in Computer physics communications (01.02.2019)
    “… Rings can be easily interacted or embedded in applications in high-energy physics and other research areas via a simple API with fully typed hierarchy of algebraic structures and algorithms for commutative algebra…”
    Get full text
    Journal Article
  12. 12
  13. 13

    Rings: an efficient Java/Scala library for polynomial rings by Poslavsky, Stanislav

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 21.09.2018
    Published in arXiv.org (21.09.2018)
    “… \Rings can be easily interacted or embedded in applications in high-energy physics and other research areas via a simple API with fully typed hierarchy of algebraic structures and algorithms for commutative algebra…”
    Get full text
    Paper
  14. 14

    Computational techniques for studying protein-protein interactions

    ISBN: 9780323902649, 0323902642
    Published: United States Elsevier Science & Technology 2022
    Get full text
    Book Chapter
  15. 15

    Chapter 9 - Computational techniques for studying protein-protein interactions by Al-Khafaji, Khattab, Taskin-Tok, Tugba

    ISBN: 9780323902649, 9780323902656, 0323902650, 0323902642
    Published: Elsevier Inc 2022
    “… The developers, engineers, and scientists are working to develop algorithms and methodological strategies for PPIs analysis, where the laws…”
    Get full text
    Book Chapter