5 citations · 7 across the 4 of their papers we have counts for
7 papers
Lagrangian Attention Tensor Networks for Velocity Gradient Statistical Modeling
Criston Hyett, Yifeng Tian, Michael Woodward +4
Direct numerical simulation of turbulence at realistic Reynolds numbers is still beyond current computational capability, necessitating models that reduce the number of resolved sp…
Lagrangian Large Eddy Simulations via Physics Informed Machine Learning
Yifeng Tian, Michael Woodward, Mikhail Stepanov +4
High Reynolds Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interest…
Physics informed machine learning with Smoothed Particle Hydrodynamics: Hierarchy of reduced Lagrangian models of turbulence
Michael Woodward, Yifeng Tian, Criston Hyett +4
Building efficient, accurate and generalizable reduced order models of developed turbulence remains a major challenge. This manuscript approaches this problem by developing a hiera…
Analysis of spatial correlations in a model 2D liquid through eigenvalues and eigenvectors of atomic level stress matrices
V. A. Levashov, M. G. Stepanov
Considerations of local atomic level stresses associated with each atom represent a particular approach to address structures of disordered materials at the atomic level. We studie…
Vorticity statistics in the direct cascade of two-dimensional turbulence
Gregory Falkovich, Vladimir Lebedev, Mikhail Stepanov
For the steady-state direct cascade of two-dimensional Navier-Stokes turbulence, we derive analytically the probability of strong vorticity fluctuations. The probability density fu…
Autler - Townes doublet probed by strong field
M. Stepanov
This paper deals with the Autler - Townes doublet structure. Applied driving and probing laser fields can have arbitrary intensities. The explanation is given of the broadening of…