6 papers
Parallel Sparse and Data-Sparse Factorization-based Linear Solvers
Xiaoye Sherry Li, Yang Liu
Efficient solutions of large-scale, ill-conditioned and indefinite algebraic equations are ubiquitously needed in numerous computational fields, including multiphysics simulations,…
Deep learning approaches to extract nuclear deformation parameters from initial-state information in heavy-ion collisions
Jun-Qi Tao, Yang Liu, Yu Sha +5
The deformation of heavy nuclei leaves characteristic imprints on the initial conditions of relativistic heavy-ion collisions. However, event-by-event fluctuations make the quantit…
Efficient Tensor Completion Algorithms for Highly Oscillatory Operators
Navjot Singh, Edgar Solomonik, Xiaoye Sherry Li +1
This paper presents low-complexity tensor completion algorithms and their efficient implementation to reconstruct highly oscillatory operators discretized as matrices.…
A deep learning approach for predicting multiple observables in Au+Au collisions at RHIC
Jun-Qi Tao, Xiang Fan, Yang Liu +4
We develop a neural network model, based on the processes of high-energy heavy-ion collisions, to study and predict several experimental observables in Au+Au collisions. We present…
Matrix-free Neural Preconditioner for the Dirac Operator in Lattice Gauge Theory
Yixuan Sun, Srinivas Eswar, Yin Lin +5
Linear systems arise in generating samples and in calculating observables in lattice quantum chromodynamics~(QCD). Solving the Hermitian positive definite systems, which are sparse…
A Linear-complexity Tensor Butterfly Algorithm for Compressing High-dimensional Oscillatory Integral Operators
P. Michael Kielstra, Tianyi Shi, Hengrui Luo +2
This paper presents a multilevel tensor compression algorithm called tensor butterfly algorithm for efficiently representing large-scale and high-dimensional oscillatory integral o…