Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems

Chen, Chun-Teh; Gu, Grace X.

doi:10.1002/advs.202300439

Cited by 9 publications

(1 citation statement)

References 51 publications

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“…While we have demonstrated this methodology with a cGAN, alternative models may be substituted, such as conditional diffusion models, for which promising results have been demonstrated in computer vision, natural language generation, and medical image reconstruction [58]. In addition, physics-based models [59] may be employed to incorporate the laws of physics into the network training process, which may improve the quality of the outputs.…”

Section: Discussionmentioning

confidence: 99%

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Foo,

Shaddy,

Murgoitio-Esandi

et al. 2024

Preprint

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Techniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. To train and test the cGAN, we constructed 30,000 elasticity and phase difference image pairs, where elasticity images were generated using a parametric model to simulate artificial samples, and phase difference images were computed using finite element analysis to simulate compression applied to the artificial samples. By applying both the cGAN and algebraic methods to simulated phase difference images, our results indicate the cGAN elasticity images exhibit better spatial resolution and sensitivity. We also evaluated the cGAN on experimental phase difference images of real spheroids embedded in hydrogels and compared the cGAN elasticity with the algebraic elasticity, OCM, and confocal fluorescence microscopy, and found the cGAN elasticity is often more robust to noise, especially within stiff nuclei.

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Section: Discussionmentioning

confidence: 99%

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Foo,

Shaddy,

Murgoitio-Esandi

et al. 2024

Preprint

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Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Jiao,

Wang,

Alavi

2023

Advanced Materials

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Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought‐after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real‐life applications. Artificial intelligence‐enabled inverse design is a powerful tool to realize performance‐oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics‐informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four‐mode analytical models by physics‐informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence‐enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics‐informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real‐life applications is discussed.

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Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems

Chen

2023

Advanced Science

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Elastography is a medical imaging technique used to measure the elasticity of tissues by comparing ultrasound signals before and after a light compression. The lateral resolution of ultrasound is much inferior to the axial resolution. Current elastography methods generally require both axial and lateral displacement components, making them less effective for clinical applications. Additionally, these methods often rely on the assumption of material incompressibility, which can lead to inaccurate elasticity reconstruction as no materials are truly incompressible. To address these challenges, a new physics-informed deep-learning method for elastography is proposed. This new method integrates a displacement network and an elasticity network to reconstruct the Young's modulus field of a heterogeneous object based on only a measured axial displacement field. It also allows for the removal of the assumption of material incompressibility, enabling the reconstruction of both Young's modulus and Poisson's ratio fields simultaneously. The authors demonstrate that using multiple measurements can mitigate the potential error introduced by the "eggshell" effect, in which the presence of stiff material prevents the generation of strain in soft material. These improvements make this new method a valuable tool for a wide range of applications in medical imaging, materials characterization, and beyond.

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Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems

Cited by 9 publications

References 51 publications

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems

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