427 citations · 942 across the 8 of their papers we have counts for
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cs.LG2020★ 72 cited
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,…
physics.comp-ph2020★ 190 cited
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…