We present a deep learning framework for quantifying and propagating uncertainty in
systems governed by non-linear differential equations using physics-informed neural …

This review discusses the lattice Boltzmann–particle dynamics (LBPD) multiscale paradigm
for the simulation of complex states of flowing matter at the interface between physics …

We present OpenRBC, a coarse-grained molecular dynamics code, which is capable of
performing an unprecedented in silico experiment—simulating an entire mammal red blood …

We present a computational framework for the simulation of blood flow with fully resolved
red blood cells (RBCs) using a modular approach that consists of a lattice Boltzmann solver …

Mesoscopic numerical simulations provide a unique approach for the quantification of the
chemical influences on red blood cell functionalities. The transport Dissipative Particle …

摘要耗散粒子动力学(dissipative particle dynamics, DPD) 是**年发展起来的一种介观尺度的
数值模拟方法, 是研究软物质和复杂流体动力学行为的一种重要手段. 这种新型介观模拟方法 …

The study of viscous fluid flow coupled with rigid or deformable solids has many applications
in biological and engineering problems, eg, blood cell transport, drug delivery, and …

Cell mechanics has proven to be important in many biological processes. Although there is
a number of experimental techniques which allow us to study mechanical properties of cell …

We develop a bottom-up coarse graining framework to construct mesoscopic force fields
directly from microscopic dynamics by a machine learning technique. This scheme, called …

We propose a highly versatile computational framework for the simulation of cellular blood
flow focusing on extreme performance without compromising accuracy or complexity. The …