Showing 1–20 of 20 results
/ Date/ Name
Jan 27, 2026Convolutional causal learning for aerodynamic flowsJan 5, 2026Data-driven sparse modeling and decomposition for superspreading-wetting dynamics of a dropletDec 10, 2025Data-driven time-dependent bases for turbulent airfoil wake-extreme vortex gust interactionsMay 30, 2025Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flowsMay 1, 2025Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow controlSep 7, 2024Single-snapshot machine learning for super-resolution of turbulenceFeb 28, 2024Data-driven nonlinear turbulent flow scaling with Buckingham Pi variablesMay 13, 2023Grasping Extreme Aerodynamics on a Low-Dimensional ManifoldMay 9, 2023Sparse sensor reconstruction of vortex-impinged airfoil wake with machine learningJan 26, 2023Super-Resolution Analysis via Machine Learning: A Survey for Fluid FlowsSep 16, 2021Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regressionMar 16, 2021Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networksJan 3, 2021Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learningDec 16, 2020Probabilistic neural network-based reduced-order surrogate for fluid flowsNov 20, 2020Model order reduction with neural networks: Application to laminar and turbulent flowsOct 26, 2020Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flowOct 23, 2020Sparse identification of nonlinear dynamics with low-dimensionalized flow representationsJun 12, 2020Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field dataApr 24, 2020Machine learning based spatio-temporal super resolution reconstruction of turbulent flowsJan 27, 2020Assessment of supervised machine learning methods for fluid flows