Results 131 to 140 of about 8,084,029 (292)

Memristive‐Gated RC‐Delay Synaptic Transistors for Time‐Encoded Analog in‐Memory Computing

Advanced Functional Materials, EarlyView.
A Memristive‐Gated Transistor for Time‐Encoded Analog In‐Memory Computing — By exploiting the RC delay of a self‐rectifying interface‐type memristor, nonlinear I–V distortion is structurally bypassed, enabling 3‐bit nonvolatile memory, spike‐timing‐based analog encoding, and hardware‐calibrated reservoir‐computing validation within a unified device ...
Yun‐Seo Shin, Eunjoo Yoo, Ki‐Hoon Son, Hyun‐Sik Kim, Dae‐Hee Han, Jeong Hyeon Son, Seung Hwan Lee, Hong‐Sub Lee   +7 more
wiley   +1 more source

Densely Multiplied Physics Informed Neural Networks

CoRR
15 pages, 9 ...
Feilong Jiang, Xiaonan Hou, Min Xia 0001
openaire   +2 more sources

Emerging Post‐CMOS Hardware Neurons for Brain‐Inspired Computing: Devices, Circuits, and System Integration

Advanced Functional Materials, EarlyView.
The physical realization of artificial neurons is a critical challenge for energy‐efficient neuromorphic computing. This review presents a comprehensive analysis of the evolution of artificial neuron implementations from conventional CMOS to emerging post‐CMOS technologies.
Kannan Udaya Mohanan, Hocheon Yoo, Benoît H. Lessard, Ioannis Kymissis, Chang‐Hyun Kim   +4 more
wiley   +1 more source

Dual-Balancing for Physics-Informed Neural Networks

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Physics-informed neural networks (PINNs) have emerged as a new learning paradigm for solving partial differential equations (PDEs) by enforcing the constraints of physical equations, boundary conditions (BCs), and initial conditions (ICs) into the loss function. Despite their successes, vanilla PINNs still suffer from poor accuracy and slow convergence
Chenhong Zhou, Jie Chen 0026, Zaifeng Yang, Ching Eng Png   +3 more
openaire   +3 more sources

Bio‐Inspired Artificial Ionic Mechanoreceptor

Advanced Functional Materials, EarlyView.
A skin‐inspired artificial mechanoreceptor based on ionic interactions is presented for biomimetic tactile sensing. Pressure‐driven ionic redistribution within microfluidic channels generates a self‐powered electrical signal without external bias. The generated waveform exhibits mechanoreceptor‐like temporal features, including overshoot and undershoot,
Mohammad Akbari, Jeppe Don, Lasse A. N. Pedersen, John Noee, Rassoul Tabassian   +4 more
wiley   +1 more source

Replication Data for: A hybrid physics-informed neural network for main bearing fatigue prognosis under grease quality variation

Replication Data for manuscript titled "A hybrid physics-informed neural network for main bearing fatigue prognosis under grease quality variation".
Yucesan, Yigit
core   +1 more source

Physics-Informed Neural Network for Solving a One-Dimensional Solid Mechanics Problem

Modelling
Our objective in this work is to demonstrate how physics-informed neural networks, a type of deep learning technology, can be utilized to examine the mechanical properties of a helicopter blade.
Vishal Singh, Dineshkumar Harursampath, Sharanjeet Dhawan, Manoj Sahni, Sahaj Saxena, Rajnish Mallick   +5 more
doaj   +1 more source

Loss-attentional physics-informed neural networks

Journal of Computational Physics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Song, Y., Wang, H., Yang, H., Taccari, M.L., Chen, X.   +4 more
openaire   +4 more sources

Reservoir‐Driven Neuromorphic Computing Based on Composite Rare‐Earth/Transition Metal Oxide Memristor

Advanced Functional Materials, EarlyView.
A defect‐engineered Ag/Gd2O3:Nb2O5/Pt rare earth composite oxide memristor enables stable multilevel reservoir states through pulse driven conductance modulation. Experimentally measured device responses are incorporated into a device aware reservoir computing framework for CIFAR‐100 image classification, highlighting the potential of rare earth ...
Hammad Ghazanfar, Haider Abbas, Muhammad Rabeel, Honggyun Kim, Ghulam Dastgeer, Muhammad Nasim, Furqan Zahoor, Yawar Abbas, Waqas Ahmad, Deok‐kee Kim   +9 more
wiley   +1 more source

Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Advanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang, Hao Li   +3 more
wiley   +1 more source