14 citations · 14 across the 6 of their papers we have counts for
6 papers
Fast, Accurate, and Scalable Fermionic Neural Networks via Translation Equivariance
David D. Dai, Yen-Ting Lin, Marin Soljačić
We demonstrate that designing a neural quantum state to be an exact eigenstate of the Hamiltonian's symmetries significantly improves both training speed and final variational ener…
Learning turbulent transport via Mori--Zwanzig graph neural networks
André Freitas, Xander M. de Wit, Alessandro Gabbana +4
We introduce a Mori--Zwanzig graph neural network (MZ--GNN) framework for learning reduced-order Lagrangian dynamics of tracer particles in homogeneous isotropic turbulence. The mo…
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…
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…
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…
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…