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Explore deep learning techniques for discovering effective coordinate systems in dynamical systems, enabling simpler models and physical law discovery. Focus on autoencoders and physics-informed machine learning.
Explore a novel method combining deep learning and symbolic regression to uncover physical laws from data, with applications in physics and cosmology.
Explore how deep learning revolutionizes turbulence modeling in fluid dynamics, focusing on RANS equations and large eddy simulations for advanced computational techniques.
Explore turbulence closure models, focusing on RANS and LES approaches for approximating complex fluid dynamics. Learn key concepts and applications in scientific computing.
Explore turbulence in fluid dynamics through canonical examples and real-world applications. Discover engineering implications and gain insights into this fascinating phenomenon.
Explore the fundamental characteristics of turbulent fluid dynamics, their prevalence in nature and engineering, and the role of Reynolds number in controlling complexity.
Explore neural networks in deep reinforcement learning for control systems, from game-playing AI to robotics. Learn about key algorithms and groundbreaking applications.
Learn to integrate multiscale differential equations efficiently using a novel deep learning architecture, with benchmarks on illustrative dynamical systems.
Explore machine learning applications in fluid dynamics modeling and control, covering patterns, complexity, RANS closure models, SINDY, and deep MPC for flow control.
Explore machine learning applications in fluid dynamics, focusing on pattern extraction and coherent structure identification in high-dimensional systems.
Explore data-driven modeling for morphing wings, focusing on reduced-order aeroelastic techniques and their application in model predictive control for improved aircraft performance.
Overview of machine learning applications in fluid mechanics, exploring historical context, pattern recognition, reduced-order modeling, and flow control, with insights on AI's impact on this data-rich field.
Discover effective coordinate systems and models simultaneously using SINDy autoencoders, a novel approach for analyzing high-dimensional data in dynamical systems.
Exploring COVID-19 control strategies using control theory principles, including model predictive control, robustness, and addressing time delays in pandemic management.
Explore Koopman operator theory for dynamical systems, its recent resurgence, and applications like Dynamic Mode Decomposition in this comprehensive overview.
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