What are the current advancements in machine learning techniques applied to fluid mechanics?
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Current advancements in machine learning techniques applied to fluid mechanics include enhancing measurement techniques' fidelity, improving experimental design, enabling real-time estimation and control, and addressing missing information in partially observable systems. Researchers are exploring methods like deep learning for simulating fluid dynamics, with physics-driven models minimizing residuals of governing equations and data-driven models providing fast solutions based on observed physical properties. These approaches aim to accelerate iterative solvers, enhance turbulence modeling, and automate traditionally complex tasks like image synthesis and natural language processing. Challenges remain, such as poor extrapolation in data-driven simulators and difficulties in training against turbulent flows, prompting ongoing efforts to enhance these techniques.
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28 Mar 2023 | Machine learning advancements in fluid mechanics include enhancing measurement fidelity, improving experimental design with digital twins, and enabling real-time estimation and control, as outlined in the paper. |
| Current advancements include physics- and data-driven deep learning methods for fluid dynamics simulation, enhancing numerical solvers, turbulence modeling, and iterative solver acceleration, with ongoing efforts to address challenges. | |
| Advancements include using Convolutional Neural Networks (CNNs) for reduced order modeling in fluid flows, extracting low-dimensional dynamics essential for understanding unsteady phenomena and flow control design. | |
3 Citations | Machine learning enhances fluid mechanics by improving measurement techniques, experimental design, and real-time estimation/control, showcasing advancements in experimental fluid dynamics. |
| Machine learning techniques are aiding fluid mechanics by modeling missing data in complex flows. The paper reviews ML algorithms, their benefits, challenges, and potential applications in this field. |
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