76 citations · 203 across the 11 of their papers we have counts for
11 papers
Mathematical analysis and numerical methods for the computation of transport coefficients in molecular dynamics
Noe Blassel, Louis Carillo, Shiva Darshan +7
We review various numerical approaches to compute transport coefficients in molecular dynamics. These approaches can be broadly classified into three groups: (i) nonequilibrium met…
Improving the stability of the covariance-controlled adaptive Langevin thermostat for large-scale Bayesian sampling
Jiani Wei, Xiaocheng Shang
Stochastic gradient Langevin dynamics and its variants approximate the likelihood of an entire dataset, via random (and typically much smaller) subsets, in the setting of Bayesian…
Accurate and robust splitting methods for the generalized Langevin equation with a positive Prony series memory kernel
Manh Hong Duong, Xiaocheng Shang
We study numerical methods for the generalized Langevin equation (GLE) with a positive Prony series memory kernel, in which case the GLE can be written in an extended variable Mark…
Accurate and efficient splitting methods for dissipative particle dynamics
Xiaocheng Shang
We study numerical methods for dissipative particle dynamics (DPD), which is a system of stochastic differential equations and a popular stochastic momentum-conserving thermostat f…
Time correlation functions of equilibrium and nonequilibrium Langevin dynamics: Derivations and numerics using random numbers
Xiaocheng Shang, Martin Kröger
We study the time correlation functions of coupled linear Langevin dynamics without and with inertia effects, both analytically and numerically. The model equation represents the p…
Structure-preserving integrators for dissipative systems based on reversible-irreversible splitting
Xiaocheng Shang, Hans Christian Öttinger
We study the optimal design of numerical integrators for dissipative systems, for which there exists an underlying thermodynamic structure known as GENERIC (general equation for th…
Assessing numerical methods for molecular and particle simulation
Xiaocheng Shang, Martin Kröger, Benedict Leimkuhler
We discuss the design of state-of-the-art numerical methods for molecular dynamics, focusing on the demands of soft matter simulation, where the purposes include sampling and dynam…
Pairwise adaptive thermostats for improved accuracy and stability in dissipative particle dynamics
Benedict Leimkuhler, Xiaocheng Shang
We examine the formulation and numerical treatment of dissipative particle dynamics (DPD) and momentum-conserving molecular dynamics. We show that it is possible to improve both th…
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling
Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler +1
Monte Carlo sampling for Bayesian posterior inference is a common approach used in machine learning. The Markov Chain Monte Carlo procedures that are used are often discrete-time a…
Adaptive Thermostats for Noisy Gradient Systems
Benedict Leimkuhler, Xiaocheng Shang
We study numerical methods for sampling probability measures in high dimension where the underlying model is only approximately identified with a gradient system. Extended stochast…
On the numerical treatment of dissipative particle dynamics and related systems
Benedict Leimkuhler, Xiaocheng Shang
We review and compare numerical methods that simultaneously control temperature while preserving the momentum, a family of particle simulation methods commonly used for the modelli…