Zobrazeno 1 - 10
of 141
pro vyhledávání: '"Tian, Yifeng"'
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 velocity field that deterministically transports samples from a simple in
Externí odkaz:
http://arxiv.org/abs/2405.06672
Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning
Autor:
Tian, Yifeng, Bagchi, Soumendu, Myhill, Liam, Po, Giacomo, Martinez, Enrique, Lin, Yen Ting, Mathew, Nithin, Perez, Danny
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 deformation. Traditional approaches for deriving mobility laws rely on ph
Externí odkaz:
http://arxiv.org/abs/2403.14015
Autor:
Woodward, Michael, Tian, Yifeng, Lin, Yen Ting, Hader, Christoph, Fasel, Hermann, Livescu, Daniel
We introduce the Mori-Zwanzig (MZ) Modal Decomposition (MZMD), a novel technique for performing modal analysis of large scale spatio-temporal structures in complex dynamical systems, and show that it represents an efficient generalization of Dynamic
Externí odkaz:
http://arxiv.org/abs/2311.09524
Autor:
Woodward, Michael, Tian, Yifeng, Lin, Yen Ting, Mohan, Arvind, Hader, Christoph, Fasel, Hermann, Chertkov, Michael, Livescu, Daniel
Understanding, predicting and controlling laminar-turbulent boundary-layer transition is crucial for the next generation aircraft design. However, in real flight experiments, or wind tunnel tests, often only sparse sensor measurements can be collecte
Externí odkaz:
http://arxiv.org/abs/2309.15864
Data-Driven Mori-Zwanzig: Approaching a Reduced Order Model for Hypersonic Boundary Layer Transition
Autor:
Woodward, Michael, Tian, Yifeng, Mohan, Arvind, Lin, Yen Ting, Hader, Christoph, Fasel, Hermann, Chertkov, Misha, Livescu, Daniel
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 generalized Koopman's description of dynamical systems. The MZ formal
Externí odkaz:
http://arxiv.org/abs/2301.07203
Infrastructure is critical for enabling society to function and the economy to thrive, but there is an increasing mismatch between the need for infrastructure investments and available capital, which is in consequence of constraints on public resourc
Externí odkaz:
http://arxiv.org/abs/2208.04709
Spurred by the emerging blockchain technology and increased interest in tokenization, this forecasting research built on extensive literature and aggregated expertise to explore the potential implementation of blockchain-enabled tokenization in infra
Externí odkaz:
http://arxiv.org/abs/2208.04710
Autor:
Tian, Yifeng, Woodward, Michael, Stepanov, Mikhail, Fryer, Chris, Hyett, Criston, Livescu, Daniel, Chertkov, Michael
High Reynolds Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved s
Externí odkaz:
http://arxiv.org/abs/2207.04012
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 to extract the Markov and memory operators for any regression models.
Externí odkaz:
http://arxiv.org/abs/2205.05135
Autor:
Woodward, Michael, Tian, Yifeng, Hyett, Criston, Fryer, Chris, Livescu, Daniel, Stepanov, Mikhail, Chertkov, Michael
Building efficient, accurate and generalizable reduced order models of developed turbulence remains a major challenge. This manuscript approaches this problem by developing a hierarchy of parameterized reduced Lagrangian models for turbulent flows, a
Externí odkaz:
http://arxiv.org/abs/2110.13311