9 citations · 15 across the 8 of their papers we have counts for
9 papers
Probing Neutron Skin through Event-by-Event Pion Asymmetry in Heavy-ion collisions
Xu-Hua Tian, Long-Gang Pang
In this work, we propose a novel approach for probing the neutron skin thickness of gold (Au) by analyzing the event-by-event distribution of and yield differences.…
Data-driven optimal control with neural network modeling of gradient flows
Xuping Tian, Baskar Ganapathysubramanian, Hailiang Liu
Extracting physical laws from observation data is a central challenge in many diverse areas of science and engineering. We propose Optimal Control Neural Networks (OCN) to learn th…
Adaptive Preconditioned Gradient Descent with Energy
Hailiang Liu, Levon Nurbekyan, Xuping Tian +1
We propose an adaptive step size with an energy approach for a suitable class of preconditioned gradient descent methods. We focus on settings where the preconditioning is applied…
Anderson acceleration of gradient methods with energy for optimization problems
Hailiang Liu, Jia-Hao He, Xuping Tian
Anderson acceleration (AA) as an efficient technique for speeding up the convergence of fixed-point iterations may be designed for accelerating an optimization method. We propose a…
SGEM: stochastic gradient with energy and momentum
Hailiang Liu, Xuping Tian
In this paper, we propose SGEM, Stochastic Gradient with Energy and Momentum, to solve a large class of general non-convex stochastic optimization problems, based on the AEGD metho…
An Adaptive Gradient Method with Energy and Momentum
Hailiang Liu, Xuping Tian
We introduce a novel algorithm for gradient-based optimization of stochastic objective functions. The method may be seen as a variant of SGD with momentum equipped with an adaptive…
Dynamic behavior for a gradient algorithm with energy and momentum
Hailiang Liu, Xuping Tian
This paper investigates a novel gradient algorithm, AGEM, using both energy and momentum, for addressing general non-convex optimization problems. The solution properties of the AG…
Data-driven optimal control of a SEIR model for COVID-19
Hailiang Liu, Xuping Tian
We present a data-driven optimal control approach which integrates the reported partial data with the epidemic dynamics for COVID-19. We use a basic Susceptible-Exposed-Infectious-…
AEGD: Adaptive Gradient Descent with Energy
Hailiang Liu, Xuping Tian
We propose AEGD, a new algorithm for first-order gradient-based optimization of non-convex objective functions, based on a dynamically updated energy variable. The method is shown…