Results 71 to 80 of about 10,821 (279)

Physics‐Informed Neural Network‐Enabled Forward Prediction and Inverse Design of Ring Origami

Advanced Science, EarlyView.
This work presents a KRT‐PINN framework that integrates Kirchhoff rod theory with physics‐informed neural networks for the forward prediction and inverse design of ring origami consisting of closed‐loop rods. The framework predicts stable states of segmented rings with prescribed natural‐curvature profiles and determines the natural‐curvature profiles ...
Luyuan Ning, Lu Lu, Sophie Leanza, Ruike Renee Zhao   +3 more
wiley   +1 more source

Multi-Scroll Chaotic Attractors in SC-CNN via Hyperbolic Tangent Function [PDF]

, 2018
A State Controlled-Cellular Neural Network (SC-CNN) based chaotic model for generating multi-scroll attractors via hyperbolic tangent function series is proposed in this paper. After presenting the double scroll generation, the presented SC-CNN system is
Kenan Altun, GÜNAY, ENİS, ALTUN, KENAN, Enis Günay   +3 more
core   +1 more source

Shape invariance of solvable Schrödinger equations with a generalized hyperbolic tangent superpotential

Results in Physics, 2022
Supersymmetric quantum mechanics provides a powerful method for solving the Schrödinger equation in quantum mechanics problems. In this paper, a hyperbolic tangent superpotential is generalized according to a new hyperbolic tangent superpotential.
Shi-Kun Zhong, Ting-Yao Xie, Li Dong, Chen-Xi Yang, Lu-Lin Xiong, Min Li, Guang Luo   +6 more
doaj   +1 more source

Advantages of Dual Hyperbolic Tangent Function Over Single Hyperbolic Tangent Function in Description of Hysteresis Loops

International Review of Electrical Engineering (IREE), 2016
If the mathematical description of the magnetic hysteresis loop branches should be simple, easy to use and should credibly describe the waveform magnetic hysteresis loop with a minimum number of parameters, then it inevitably leads to the description of magnetic hysteresis loop branches with a hyperbolic tangent function.
Barić, Tomislav, Boras, Vedran, Glavaš, Hrvoje   +2 more
openaire   +1 more source

DDSurfer: A Weakly‐Supervised Dual‐Stream Deep Learning Framework for Cortical Surface Reconstruction From Diffusion MRI

Advanced Science, EarlyView.
DDSurfer reconstructs cortical surfaces directly from diffusion MRI without requiring T1‐weighted scans. By fusing complementary microstructural features and learning diffeomorphic deformations, it efficiently generates accurate white matter and pial surfaces, improving geometric fidelity and morphometric reliability across datasets for robust surface ...
Chengjin Li, Wei Zhang, Xi Zhu, Yuqian Chen, Nir A. Sochen, Jarrett Rushmore, Carl‐Fredrik Westin, Yogesh Rathi, Lauren J. O'Donnell, Ofer Pasternak, Fan Zhang   +10 more
wiley   +1 more source

Higher Derivatives of the Tangent and Inverse Tangent Functions and Chebyshev Polynomials [PDF]

, 2023
The higher derivatives of the tangent and hyperbolic tangent functions are determined. Formulas for the higher derivatives of the inverse tangent and inverse hyperbolic tangent functions as polynomials are stated and proved. Using another formula for the
Kronenburg, M. J.
core   +1 more source

Enhancing Electrical Impedance Based Deformation Sensing with Dielectric Current Guide

Advanced Electronic Materials, EarlyView.
This work introduces a dielectric‐based field manipulation strategy for electrical impedance tomography in soft robotics. By using silicone as a passive electric field guide, improved deformation sensing accuracy and reduced interference are achieved without embedding conductive components.
Arsen Abdulali, David Hardman, Yue Xie, Fumiya Iida   +3 more
wiley   +1 more source

Optimal bounds for the sine and hyperbolic tangent means [PDF]

, 2020
We provide the optimal bounds for the sine and hyperbolic tangent means in terms of various weighted means of the arithmetic and root-mean square ...
Alfred Witkowski; Department of Mathematics, Institute of Mathematics and Physics UTP University of Science and Technology, Bydgoszcz, Poland, Monika Nowicka; Department of Mathematics, Institute of Mathematics and Physics UTP University of Science and Technology, Bydgoszcz, Poland   +1 more
core  

Novel Analog Implementation of a Hyperbolic Tangent Neuron in Artificial Neural Networks [PDF]

, 2021
Recently, enormous datasets have made power dissipation and area usage lie at the heart of designs for artificial neural networks (ANNs). Considering the significant role of activation functions in neurons and the growth of hardware-based neural networks
Zhou, Mengchu, Shakiba, Fatemeh Mohammadi   +1 more
core   +1 more source

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source