25 citations · 45 across the 11 of their papers we have counts for
11 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…
Mori-Zwanzig mode decomposition: Comparison with time-delay embeddings
Michael Woodward, Yen Ting Lin, Yifeng Tian +3
We introduce the Mori-Zwanzig Mode Decomposition (MZMD), a novel data-driven technique for efficient modal analysis of and reduced-order modeling of large-scale spatio-temporal dyn…
Data-Driven Mori-Zwanzig: Reduced Order Modeling of Sparse Sensors Measurements for Boundary Layer Transition
Michael Woodward, Yifeng Tian, Yen Ting Lin +5
Understanding, predicting and controlling laminar-turbulent boundary-layer transition is crucial for the next generation aircraft design. However, in real flight experiments, or wi…
Data-Driven Mori-Zwanzig: Approaching a Reduced Order Model for Hypersonic Boundary Layer Transition
Michael Woodward, Yifeng Tian, Arvind Mohan +5
In this work, we apply, for the first time to spatially inhomogeneous flows, a recently developed data-driven learning algorithm of Mori-Zwanzig (MZ) operators, which is based on a…
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…
Regression-based projection for learning Mori-Zwanzig operators
Yen Ting Lin, Yifeng Tian, Danny Perez +1
We propose to adopt statistical regression as the projection operator to enable data-driven learning of the operators in the Mori--Zwanzig formalism. We present a principled method…
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…
Data Driven Learning of Mori-Zwanzig Operators for Isotropic Turbulence
Yifeng Tian, Yen Ting Lin, Marian Anghel +1
Developing reduced-order models for turbulent flows, which contain dynamics over a wide range of scales, is an extremely challenging problem. In statistical mechanics, the Mori-Zwa…
Physics-Informed Machine Learning of the Lagrangian Dynamics of Velocity Gradient Tensor
Yifeng Tian, Daniel Livescu, Michael Chertkov
Reduced models describing the Lagrangian dynamics of the Velocity Gradient Tensor (VGT) in Homogeneous Isotropic Turbulence (HIT) are developed under the Physics-Informed Machine L…
Data-driven learning for the Mori-Zwanzig formalism: a generalization of the Koopman learning framework
Yen Ting Lin, Yifeng Tian, Marian Anghel +1
A theoretical framework which unifies the conventional Mori-Zwanzig formalism and the approximate Koopman learning is presented. In this framework, the Mori-Zwanzig formalism, deve…
Density Effects on the Post-shock Turbulence Structure and Dynamics
Yifeng Tian, Farhad A. Jaberi, Daniel Livescu
Turbulence structure resulting from multi-fluid or multi-species, variable-density isotropic turbulence interaction with a Mach 2 shock is studied using turbulence-resolving shock-…