72 citations · 144 across the 3 of their papers we have counts for
4 papers
Physics-informed neural networks (PINNs) for fluid mechanics: A review
Shengze Cai, Zhiping Mao, Zhicheng Wang +2
Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier-Stokes equations (NSE), we still cannot incorporate…
Multiscale Parareal Algorithm for Long-Time Mesoscopic Simulations of Microvascular Blood Flow in Zebrafish
Ansel Blumers, Minglang Yin, Hiroyuki Nakajima +3
Various biological processes such as transport of oxygen and nutrients, thrombus formation, vascular angiogenesis and remodeling are related to cellular/subcellular level biologica…
Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging
Enrui Zhang, Minglang Yin, George Em Karniadakis
We apply Physics-Informed Neural Networks (PINNs) for solving identification problems of nonhomogeneous materials. We focus on the problem with a background in elasticity imaging,…
Non-invasive Inference of Thrombus Material Properties with Physics-informed Neural Networks
Minglang Yin, Xiaoning Zheng, Jay D. Humphrey +1
We employ physics-informed neural networks (PINNs) to infer properties of biological materials using synthetic data. In particular, we successfully apply PINNs on inferring the thr…