4 citations · 6 across the 3 of their papers we have counts for
5 papers
Liouville Flow Importance Sampler
Yifeng Tian, Nishant Panda, Yen Ting Lin
We present the Liouville Flow Importance Sampler (LFIS), an innovative flow-based model for generating samples from unnormalized density functions. LFIS learns a time-dependent vel…
Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning
Yifeng Tian, Soumendu Bagchi, Liam Myhill +5
Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic def…
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…