4 papers
A practical investigation on time integration in the quantized tensor train format
Erika Ye
Quantized tensor trains (QTTs) are a multiscale computational framework that can potentially reduce the computational cost of solving partial differential equations and initial val…
RELift: Learned Coarse-to-Fine Propagators for Time-Dependent PDEs with Applications to Electron Dynamics
Hardeep Bassi, Yuanran Zhu, Erika Ye +5
We present RELift (Restrict, Evolve, Lift), a two-phase learning framework that couples coarse-grid numerical solvers with neural operators to super-resolve and forecast fine-grid…
Time integration of quantized tensor trains using the interpolative dynamical low-rank approximation
Erika Ye, Chao Yang
Quantized tensor trains (QTTs) are a low-rank and multiscale framework that allows for efficient approximation and manipulation of multi-dimensional, high resolution data. One area…
Inexact subspace projection methods for low-rank tensor eigenvalue problems
Alec Dektor, Peter DelMastro, Erika Ye +2
We propose inexact subspace iteration for solving high-dimensional eigenvalue problems with low-rank structure. Inexactness stems from low-rank compression, enabling efficient repr…