Liouville Flow Importance Sampler

no code implementations • 3 May 2024 • 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.

Regression-based projection for learning Mori-Zwanzig operators

no code implementations • 10 May 2022 • Yen Ting Lin, Yifeng Tian, Danny Perez, Daniel Livescu

We propose to adopt statistical regression as the projection operator to enable data-driven learning of the operators in the Mori--Zwanzig formalism.

regression

Physics informed machine learning with Smoothed Particle Hydrodynamics: Hierarchy of reduced Lagrangian models of turbulence

1 code implementation • 25 Oct 2021 • Michael Woodward, Yifeng Tian, Criston Hyett, Chris Fryer, Daniel Livescu, Mikhail Stepanov, Michael Chertkov

Within this hierarchy, two new parameterized smoothing kernels are developed in order to increase the flexibility of the learn-able SPH simulators.

BIG-bench Machine Learning Physics-informed machine learning