427 citations · 942 across the 8 of their papers we have counts for
8 papers
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes
Minglang Yin, Zongren Zou, Enrui Zhang +3
Quantifying biomechanical properties of the human vasculature could deepen our understanding of cardiovascular diseases. Standard nonlinear regression in constitutive modeling requ…
Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems
Minglang Yin, Enrui Zhang, Yue Yu +1
Multiscale modeling is an effective approach for investigating multiphysics systems with largely disparate size features, where models with different resolutions or heterogeneous d…
A physics-informed variational DeepONet for predicting the crack path in brittle materials
Somdatta Goswami, Minglang Yin, Yue Yu +1
Failure trajectories, identifying the probable failure zones, and damage statistics are some of the key quantities of relevance in brittle fracture applications. High-fidelity nume…
Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network
Minglang Yin, Ehsan Ban, Bruno V. Rego +4
Aortic dissection progresses via delamination of the medial layer of the wall. Notwithstanding the complexity of this process, insight has been gleaned by studying in vitro and in…
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…