Results 121 to 130 of about 8,084,029 (292)

Optoelectronic Synaptic Devices Using Molecular Telluride Phase‐Change Inks for Three‐Factor Learning

Advanced Functional Materials, EarlyView.
Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner, Dhananjeya Kumaar, Giacomo Torti, Killian Keller, Till Zellweger, Florian M. Schenk, Christoph Weilenmann, Daniel Chelladurai, Vara Prasad Jonnalagadda, Ghazi Sarwat Syed, Vanessa Wood, Jürg Leuthold, Alexandros Emboras, Maksym Yarema, Mathieu Luisier   +14 more
wiley   +1 more source

Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks

CoRR
This article introduces Perception-Informed Neural Networks (PrINNs), a framework designed to incorporate perception-based information into neural networks, addressing both systems with known and unknown physics laws or differential equations. Moreover, PrINNs extend the concept of Physics-Informed Neural Networks (PINNs) and their variants, offering a
Mehran Mazandarani, Marzieh Najariyan
openaire   +2 more sources

Implantable Ionic Memristors Based on Natural Polymer Heterojunctions

Advanced Functional Materials, EarlyView.
We report an implantable natural polymer‐based ionic memristor composed of hyaluronic acid, chitosan, and PDMS. The device achieved 98.94% accuracy in MNIST classification while reducing training time by 36.8% compared with a conventional artificial neural network (ANN).
Dong‐yup Lee, Byung Ik Park, Gihyeon Yim, Hyunjae Yoo, Sangjun Ma, Jundong Park, Jeong‐Yun Sun   +6 more
wiley   +1 more source

Biodegradable and Biocompatible Functional Polymers for Biomedical Applications

Advanced Functional Materials, EarlyView.
Biodegradable and biocompatible functional polymers integrate electrical, mechanical, and stimuli‐responsive functionalities while enabling programmed degradation under physiological conditions. This review introduces recent advances in conductive, shape‐memory, self‐healing, photocurable, and adhesive polymer systems, emphasizing material design ...
Won Bae Han, Shuvra Mondal, Sungkeun Han, Chan‐Hwi Eom, Venkata Ramesh Naganaboina, Suk‐Won Hwang   +5 more
wiley   +1 more source

Physics-Informed Neural Network Surrogate Modeling of Three-Dimensional Gas–Liquid Hydrodynamics in Aerated Double-Impeller Stirred Tanks

ChemEngineering
This study develops a physics-informed surrogate for predicting three-dimensional gas–liquid hydrodynamics in an aerated double-impeller stirred tank. Reference fields were generated using steady-state computational fluid dynamics (CFD) simulations in ...
Forough Sharifi, Ali Lohi, Farhad Ein-Mozaffari   +2 more
doaj   +1 more source

Multimodal Optoelectronic Synapses for Neuromorphic Intelligence: Coupling Light, Electrical, Thermal, and Beyond

Advanced Functional Materials, EarlyView.
This review revisits optoelectronic neuromorphic devices that extend from unimodal sensing toward multimodal perception. Five types of stimuli‐to‐output information flow that are classified based on the device roles within the system and applications are summarized and discussed.
Jihyun Shin, Junhyung Cho, Wangmyung Choi, Jaehyun Hur, Jae Kyeong Jeong, Ulrike Kraft, Hocheon Yoo   +6 more
wiley   +1 more source

Freeform Optics design for Laser Beam Shaping Based on Ray Mapping and Physics-informed Neural Network

We train a Physics-informed Neural Network (PINN) on ray-mapped freeform surfaces for beam shaping. By incorporating refraction law, normal integrability, and smoothness constraints in the loss function, we achieve physically consistent and ...
Hao Sun, Wei Tan, Jian Fen Xu, Long Bai
core   +1 more source

SPIKANs: separable physics-informed Kolmogorov–Arnold networks

Machine Learning: Science and Technology
Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing.
Bruno Jacob, Amanda A Howard, Panos Stinis   +2 more
doaj   +1 more source

Light‐Induced Field‐Tunneling Synapses in Solution‐Processed Van Der Waals Heterostructures for Scalable, Retina‐Inspired Optical Sensing

Advanced Functional Materials, EarlyView.
A scalable, solution‐processed WSe2/ZrO2‐x van der Waals heterostructure realizes a light‐induced field‐tunneling synapse (LIFTS) that activates exclusively under bright illumination, emulating the photopic adaptation of the human retina at the device level.
Kijeong Nam, Jihyun Kim, Soobeen Ji, In Soo Kim, Sejin Kim, Eungseon Yeon, Do Kyung Hwang, Iva Plutnarová, Zdeněk Sofer, Sang Min Won, Joohoon Kang   +10 more
wiley   +1 more source

ST-GPINN: a spatio-temporal graph physics-informed neural network for enhanced water quality prediction in water distribution systems

npj Clean Water
Data-driven models often neglect the underlying physical principles, limiting generalization capabilities in water distribution systems (WDSs). This study presents a novel spatio-temporal graph physics-informed neural network (ST-GPINN) for water quality
Tianwei Mu, Feiyu Duan, Baokuan Ning, Bo Zhou, Junyu Liu, Manhong Huang   +5 more
doaj   +1 more source