Sparse regression yields explicit invariant polynomial SGS closures for LES on anisotropic grids that achieve neural-network accuracy with simpler forms and lower computational cost.
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Turbulence Modeling in the Age of Data
6 Pith papers cite this work, alongside 1,251 external citations. Polarity classification is still indexing.
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LESnets integrates LES equations and the law of the wall into F-FNO to enable data-free, stable long-term predictions of wall-bounded turbulence at Re_tau up to 1000 on coarse grids, matching traditional LES accuracy at higher efficiency.
A data-driven framework learns a unified, frame-invariant turbulence model from sparse observations across regimes via multi-objective ensemble learning and similarity-based case selection.
A latent-space reduced-order model using autoencoders and learned dynamics enables Bayesian recovery of initial density and pressure in Sod shock tube simulations, with posterior uncertainty contracting substantially as observation density increases.
A CTA-Swin-UNet with MTFC correction and resolvent-based SLSE reconstruction achieves stable autoregressive prediction of 3D wall-bounded turbulence up to 300 time steps.
Symbolic regression with built-in physical constraints produces a non-linear turbulence closure for LBM that outperforms Smagorinsky and generalizes zero-shot to channel flow.
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Discovery of Sparse Invariant Subgrid-Scale Closures via Dissipation-Controlled Training for Large Eddy Simulation on Anisotropic Grids
Sparse regression yields explicit invariant polynomial SGS closures for LES on anisotropic grids that achieve neural-network accuracy with simpler forms and lower computational cost.
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Large-eddy simulation nets (LESnets) based on physics-informed neural operator for wall-bounded turbulence
LESnets integrates LES equations and the law of the wall into F-FNO to enable data-free, stable long-term predictions of wall-bounded turbulence at Re_tau up to 1000 on coarse grids, matching traditional LES accuracy at higher efficiency.
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Toward a unified data-driven turbulence model through multi-objective learning
A data-driven framework learns a unified, frame-invariant turbulence model from sparse observations across regimes via multi-objective ensemble learning and similarity-based case selection.
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The impact of observation density on Bayesian inversion of latent dynamics in shock-dominated flows
A latent-space reduced-order model using autoencoders and learned dynamics enables Bayesian recovery of initial density and pressure in Sod shock tube simulations, with posterior uncertainty contracting substantially as observation density increases.
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Long-horizon prediction of three-dimensional wall-bounded turbulence with CTA-Swin-UNet and resolvent analysis
A CTA-Swin-UNet with MTFC correction and resolvent-based SLSE reconstruction achieves stable autoregressive prediction of 3D wall-bounded turbulence up to 300 time steps.
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Data-driven Symbolic Closure for Turbulence Modeling in the Lattice Boltzmann Framework
Symbolic regression with built-in physical constraints produces a non-linear turbulence closure for LBM that outperforms Smagorinsky and generalizes zero-shot to channel flow.