machine learningdata-driven modelingoptimizationcontrolThe field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to ...
Perspective on machine learning for advancing fluid mechanics Physical Review Fluids(IF2.5)Pub Date : M. P. Brenner, J. D. Eldredge, and J. B. Freund A perspective is presented on how machine learning (ML) tools, with their burgeoning popularity and the increasing availability of portable imp...
Machine learning for fluid mechanics. Annu. Rev. Fluid Mech. 52, 477–508 (2020). Google Scholar Webb, S. Deep learning for biology. Nature 554, 555–557 (2018). Google Scholar Bechinger, C. et al. Active particles in complex and crowded environments. Rev. Mod. Phys. 88, 045006 ...
今天推荐一本大神 Steven L. Brunton 主编的在 2023 年出版的书: "Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning" 出版社链接: https://www.cambridge.org/core/books…
Advances in machine learning for climate physics have extended observational data records in time, space and observables, making them longer, more global and more complete. Innovative approaches that use machine learning to learn parameterizations from data or high-resolution simulations could contribute ...
The availability of large datasets has boosted the application of machine learning in many fields and is now starting to shape active-matter research as well. Machine learning techniques have already been successfully applied to active-matter data—for e
物理信息机器学习(Physics-informed machine learning,PIML),指的是将物理学的先验知识(历史上自然现象和人类行为的高度抽象),与数据驱动的机器学习模型相结合,这已经成为缓解训练数据短缺、提高模型泛化能力和确保结果的物理合理性的有效途径。在本文中,我们...
Machine learning for fluid mechanics Annu. Rev. Fluid Mech., 52 (2020), pp. 477-508, 10.1146/annurev-fluid-010719-060214 URL https://www.annualreviews.org/doi/abs/10.1146/annurev-fluid-010719-060214 View in ScopusGoogle Scholar Chilamkurthy et al., 2018 Chilamkurthy S., Ghosh R., Tan...
Machine learning facilitates data-driven techniques for handling large amounts of combustion data, either obtained through experiments or simulations under multiple spatiotemporal scales, thereby finding the hidden patterns underlying these data and promoting combustion research. This work presents an overview...
Karthik Duraisamy, A Framework for Turbulence Modeling using Big Data Anand Pratap Singh et al., Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils Computer Graphics Regression Forest Lubor Ladicky et al., Data-driven Fluid Simulations using Regression Forests Convo...