2021 journal article
Deep learning surrogate interacting Markov chain Monte Carlo based full wave inversion scheme for properties of materials quantification
JOURNAL OF SOUND AND VIBRATION, 497.
author keywords: Deep learning; Full wave inversion; Inverse problems; Kurganov-Tadmor; Markov chain Monte Carlo; Surrogate model; Wave propagation
TL;DR:
The results demonstrate the feasibility of the proposed method for estimating mechanical properties of materials with high accuracy using deep learning approaches for full Wave Inversion imaging scheme.
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open access via arxiv.org (repository)
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Source: Web Of Science
Added: March 15, 2021
Full Wave Inversion (FWI) imaging scheme has many applications in engineering, geoscience and medical sciences. In this paper, a surrogate deep learning FWI approach is presented to quantify properties of materials using stress waves. Such inverse problems, in general, are ill-posed and nonconvex, especially in cases where the solutions exhibit shocks, heterogeneity and discontinuities. The proposed approach is proven efficient to obtain global minima responses in these cases. This approach is trained based on random sampled sets of material properties and sampled trials around local minima, therefore, it requires a forward simulation can handle high heterogeneity, discontinuities and large gradients. High resolution Kurganov–Tadmor (KT) central finite volume method is used as forward wave propagation operator. Using the proposed framework, material properties of 2D media are quantified for several different situations. The results demonstrate the feasibility of the proposed method for estimating mechanical properties of materials with high accuracy using deep learning approaches.