Machine learning and quantum computing for reactive turbulence modeling and simulation

被引：4
|
作者：
Givi, Peyman [1 ]
机构：
[1] Univ Pittsburgh, Ctr Res Comp, Mech Engn & Petr Engn, Pittsburgh, PA 15261 USA
来源：
MECHANICS RESEARCH COMMUNICATIONS | 2021年 / 116卷
关键词：
DISCRETE LOGARITHMS; COMPUTATION; ALGORITHMS; NETWORKS;
D O I：
10.1016/j.mechrescom.2021.103759
中图分类号：
O3 [力学];
学科分类号：
08 ; 0801 ;
摘要：
A perspective is given on some prospects of machine learning and quantum computing for modeling and simulation of turbulent reactive flows. This perspective is a more comprehensive and extended form of the 13th Elsevier Distinguished Lecture in Mechanics delivered by the author.
引用
收藏
页数：3
相关论文
共 50 条
  • [41] On the sample complexity of quantum Boltzmann machine learning
    Coopmans, Luuk
    Benedetti, Marcello
    COMMUNICATIONS PHYSICS, 2024, 7 (01):
  • [42] QUASIM: Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing
    Maass, Wolfgang
    Agrawal, Ankit
    Ciani, Alessandro
    Danz, Sven
    Delgadillo, Alejandro
    Ganser, Philipp
    Kienast, Pascal
    Kulig, Marco
    Koenig, Valentina
    Rodellas-Gracia, Nil
    Rughubar, Rivan
    Schroeder, Stefan
    Stautner, Marc
    Stein, Hannah
    Stollenwerk, Tobias
    Zeuch, Daniel
    Wilhelm, Frank K.
    KUNSTLICHE INTELLIGENZ, 2024, : 361 - 370
  • [43] Define, refined and re-defined concepts of quantum machine learning : A review
    Chavan, Shradha
    Mulay, Preeti
    JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES, 2024, 45 (05): : 1229 - 1262
  • [44] Intelligent edge computing based on machine learning for smart city
    Lv, Zhihan
    Chen, Dongliang
    Lou, Ranran
    Wang, Qingjun
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2021, 115 : 90 - 99
  • [45] On Quantum Methods for Machine Learning Problems Part I: Quantum Tools
    Ablayev, Farid
    Ablayev, Marat
    Huang, Joshua Zhexue
    Khadiev, Kamil
    Salikhova, Nailya
    Wu, Dingming
    BIG DATA MINING AND ANALYTICS, 2020, 3 (01): : 41 - 55
  • [46] Equivalence of machine learning models in modeling chaos
    Chen, Xiaolu
    Weng, Tongfeng
    Li, Chunzi
    Yang, Huijie
    CHAOS SOLITONS & FRACTALS, 2022, 165
  • [47] Modeling machine learning: A cognitive economic approach ☆
    Caplin, Andrew
    Martin, Daniel
    Marx, Philip
    JOURNAL OF ECONOMIC THEORY, 2025, 224
  • [48] Time independent universal computing with spin chains: quantum plinko machine
    Thompson, K. F.
    Gokler, C.
    Lloyd, S.
    Shor, P. W.
    NEW JOURNAL OF PHYSICS, 2016, 18
  • [49] Addressing Machine Unavailability in Job Shop Scheduling: A Quantum Computing Approach
    Aggoune, Riad
    Deleplanque, Samuel
    METAHEURISTICS, MIC 2024, PT I, 2024, 14753 : 234 - 245
  • [50] Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights
    Laref, Slimane
    Harrou, Fouzi
    Wang, Bin
    Sun, Ying
    Laref, Amel
    Laleg-Kirati, Taous-Meriem
    Gojobori, Takashi
    Gao, Xin
    MOLECULES, 2023, 28 (08):
12345