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Elasticity

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Magnetohydrodynamic levitation for high-performance flexible pumps.

Proceedings of the National Academy of Sciences of the United States of America
We use magnetohydrodynamic levitation as a means to create a soft, elastomeric, solenoid-driven pump (ESP). We present a theoretical framework and fabrication of a pump designed to address the unique challenges of soft robotics, maintaining pumping p...

Ultrafast small-scale soft electromagnetic robots.

Nature communications
High-speed locomotion is an essential survival strategy for animals, allowing populating harsh and unpredictable environments. Bio-inspired soft robots equally benefit from versatile and ultrafast motion but require appropriate driving mechanisms and...

Highly Integrated Multi-Material Fibers for Soft Robotics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Soft robots are envisioned as the next generation of safe biomedical devices in minimally invasive procedures. Yet, the difficulty of processing soft materials currently limits the size, aspect-ratio, manufacturing throughput, as well as, the design ...

Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio.

Acta biomaterialia
Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this discovery, most existing methods appr...

Optical force estimation for interactions between tool and soft tissues.

Scientific reports
Robotic assistance in minimally invasive surgery offers numerous advantages for both patient and surgeon. However, the lack of force feedback in robotic surgery is a major limitation, and accurately estimating tool-tissue interaction forces remains a...

Deep learning-accelerated computational framework based on Physics Informed Neural Network for the solution of linear elasticity.

Neural networks : the official journal of the International Neural Network Society
The paper presents an efficient and robust data-driven deep learning (DL) computational framework developed for linear continuum elasticity problems. The methodology is based on the fundamentals of the Physics Informed Neural Networks (PINNs). For an...

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

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
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 elastog...

Unsupervised deep learning-based displacement estimation for vascular elasticity imaging applications.

Physics in medicine and biology
. Arterial wall stiffness can provide valuable information on the proper function of the cardiovascular system. Ultrasound elasticity imaging techniques have shown great promise as a low-cost and non-invasive tool to enable localized maps of arterial...

Artificial Neural Network-Aided Computational Approach for Mechanophenotyping of Biological Cells Using Atomic Force Microscopy.

Journal of biomechanical engineering
The artificial neural network (ANN) based models have shown the potential to provide alternate data-driven solutions in disease diagnostics, cell sorting and overcoming AFM-related limitations. Hertzian model-based prediction of mechanical properties...

Physics-informed UNets for discovering hidden elasticity in heterogeneous materials.

Journal of the mechanical behavior of biomedical materials
Soft biological tissues often have complex mechanical properties due to variation in structural components. In this paper, we develop a novel UNet-based neural network model for inversion in elasticity (El-UNet) to infer the spatial distributions of ...