arXiv subjects

Jing Rao

Publications and source records attributed to Jing Rao.

4 recordsLinked to original sources

arXiv2026

Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging

Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth's subsurface, acoustic imaging and non-destructive testing in material science, and ultrasound computed tomography in medicine. Current approaches for solving CWI problems can be divided into two categories: those rooted in traditional physics, and those based on deep learning. Physics-based methods stand out for their ability to provide high-resolution and quantitatively accurate estimates of acoustic properties within the medium. However, they can be computationally intensive and are susceptible to ill-posedness and nonconvexity typical of CWI problems. Machine learning-based computational methods have recently emerged, offering a different perspective to address these challenges. Diverse scientific communities have independently pursued the integration of deep learning in CWI. This review discusses how contemporary scientific machine-learning (ML) techniques, and deep neural networks in particular, have been developed to enhance and integrate with traditional physics-based methods for solving CWI problems. We present a structured framework that consolidates existing research spanning multiple domains, including computational imaging, wave physics, and data science. This study concludes with important lessons learned from existing ML-based methods and identifies technical hurdles and emerging trends through a systematic analysis of the extensive literature on this topic.

cs.LG↗

arXiv2025

Filling a gap in materials mechanics: Nanoindentation at high constant strain rates upto $10^5 s^{-1}$

A central focus in high strain rate research is understanding the dynamic behavior of materials at strain rates where a strength upturn is observed. While strength upturns at strain rates of $10^3$ to $10^4~\mathrm{s}^{-1}$ have been widely reported in the literature, their occurrence in certain materials remains controversial, and the underlying physics driving this phenomenon is not yet fully understood. Current mechanical testing methods are limited, as no single technique spans the full strain rate range of $10^1$ to $10^5~\mathrm{s}^{-1}$ where this phenomenon is expected, and a unified technique would enable consistent post-deformation characterization with minimal error. To address this, we developed a customized piezoelectric in situ nanomechanical test setup, enabling constant indentation strain rates up to $10^5~\mathrm{s}^{-1}$ for the first time. Using this system, we examined rate-dependent hardness in single-crystalline molybdenum, nanocrystalline nickel, and amorphous fused silica over strain rates from $10^1$ to $10^5~\mathrm{s}^{-1}$, remarkably revealing a hardness upturn in all three materials. Further, post-deformation analysis of single-crystalline molybdenum revealed that the hardness upturn was primarily driven by increased dislocation density, with phonon drag -- traditionally considered a dominant contributor -- playing a minimal role.

cond-mat.mtrl-sci↗

arXiv2024

Microstructural features and hydrogen diffusion in bcc FeCr alloys: a comparison between the Kelvin probe- and nanohardness based- methods

Hydrogen embrittlement can result in a sudden failure in metallic materials, which is particularly harmful in industrially relevant alloys, such as steels. A more comprehensive understanding of hydrogen interactions with microstructural features is critical for preventing hydrogen-induced damage and promoting a hydrogen-based environment-benign economy. We use the Kelvin probe-based potentiometric hydrogen electrode method and thermal desorption spectroscopy to investigate hydrogen interactions with different hydrogen traps in ferritic FeCr alloys with different chromium contents, dislocation densities, and grain sizes. In addition, we confirm the validity of a novel nanohardness-based diffusion coefficient approach by performing in situ nanoindentation testing. Simultaneous acquisition of the dynamic time-resolved mechanical response of FeCr alloys to hydrogen and the hydrogen diffusivities in these alloys is possible during continuous hydrogen supply. Dislocations, grain boundaries and Cr atoms induce reversible hydrogen trapping sites in these ferritic alloys, leading to the reduction of the hydrogen diffusion coefficients and the increase of the absorbed hydrogen.

cond-mat.mtrl-sci↗

arXiv2021

Quantitative reconstruction of defects in multi-layered bonded composites using fully convolutional network-based ultrasonic inversion

Ultrasonic methods have great potential applications to detect and characterize defects in multi-layered bonded composites. However, it remains challenging to quantitatively reconstruct defects, such as disbonds and kissing bonds, that influence the integrity of adhesive bonds and seriously reduce the strength of assemblies. In this work, an ultrasonic method based on the supervised fully convolutional network (FCN) is proposed to quantitatively reconstruct defects hidden in multi-layered bonded composites. In the training process of this method, an FCN establishes a non-linear mapping from measured ultrasonic data to the corresponding velocity models of multi-layered bonded composites. In the predicting process, the trained network obtained from the training process is used to directly reconstruct the velocity models from the new measured ultrasonic data of adhesively bonded composites. The presented FCN-based inversion method can automatically extract useful features in multi-layered composites. Although this method is computationally expensive in the training process, the prediction itself in the online phase takes only seconds. The numerical results show that the FCN-based ultrasonic inversion method is capable to accurately reconstruct ultrasonic velocity models of the high contrast defects, which has great potential for online detection of adhesively bonded composites.

cond-mat.mtrl-sci↗