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UTact: Underwater Vision‐Based Tactile Sensor with Geometry Reconstruction and Contact Force Estimation

Advanced Robotics Research, EarlyView.
Embedded flexible sensing technologies advance underwater soft robotics, yet most systems still suffer from hysteresis and limited perceptiveness. Instead, vision‐based tactile sensors provide reliable and rapid feedback essential for complex underwater tasks.
Qiyi Zhang, Zonghao Zuo, Haoxuan Wang, Bin Liu, Yilidusi Yilihamu, Li Wen   +5 more
wiley   +1 more source

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Physics-Informed Neural Networks

2021
Physics-informed neural networks (PINNs) are used for problems where data are scarce. The underlying physics is enforced via the governing differential equation, including the residual in the cost function. PINNs can be used for both solving and discovering differential equations.
Stefan Kollmannsberger, Davide D’Angella, Moritz Jokeit, Leon Herrmann   +3 more
openaire   +1 more source

Self-adaptive physics-informed neural networks

Journal of Computational Physics, 2022
Levi D. McClenny, Ulisses M. Braga-Neto
openaire   +1 more source

A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures

Engineering Fracture Mechanics, 2021
Jian-Guo Gong, Fu-Zhen Xuan
exaly  

A physics-informed neural network approach to fatigue life prediction using small quantity of samples

International Journal of Fatigue, 2023
Yazhi Li
exaly  

Prediction of optical solitons using an improved physics-informed neural network method with the conservation law constraint

Chaos, Solitons and Fractals, 2022
Nikolay A Kudryashov, Chao-Qing Dai
exaly  

Outlier-resistant physics-informed neural network

Physical Review E
Recent advances in machine learning have introduced physics-informed neural networks (PINN) as a valuable tool for addressing dynamics through governing equations and experimental observations. Outliers can be present in measurements and significantly affect the accuracy of the solutions provided by PINN.
D. H. G. Duarte, P. D. S. de Lima, J. M. de Araújo   +2 more
openaire   +2 more sources

Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear partial differential equations

Computer Methods in Applied Mechanics and Engineering, 2023
Jinshuai Bai, G R Liu, Ashish Gupta
exaly  

A Physics-Informed Neural Network-based Topology Optimization (PINNTO) framework for structural optimization

Engineering Structures, 2023
Hyogu Jeong, Jinshuai Bai, C P Batuwatta-Gamage   +2 more
exaly