Results 101 to 110 of about 18,341 (302)

Physics-Informed Neural Networks for Structural Mechanics and Construction: Modeling the Deflection of a Single-Span Beam Physics-Informed Neural Networks

Железобетонные конструкции
This article presents the development and analysis of a Physics-Informed Neural Network (PINN) model for calculating the deflection of a simply supported beam under uniformly distributed load.
F. N. Zakharov, Qian Jie, Xu Yi
doaj   +1 more source

Plasma two-fluid simulation using physics-informed neural networks [PDF]

AIP Advances
We develop a physics-informed neural network (PINN)-based framework for simulating electron–ion two-fluid plasmas with large scale separation. As a benchmark, we examine the one-dimensional diffusion of a magnetized low-temperature plasma and compute the
R. Kono, S. Isayama, S. Matsukiyo
doaj   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Development in metal multiaxial fatigue life prediction based on physics-informed neural network

Jixie qiangdu
The research on multiaxial fatigue life prediction of materials is one of the critical elements in ensuring the structural integrity of components. In recent years, machine learning, especially neural networks, has been widely applied in fatigue life ...
ZHANG Zhuanli, SUN Xingyue, CHEN Xu
doaj  

Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations

Applied Sciences
This paper establishes a method for solving partial differential equations using a multi-step physics-informed deep operator neural network. The network is trained by embedding physics-informed constraints.
Jing Wang, Yubo Li, Anping Wu, Zheng Chen, Jun Huang, Qingfeng Wang, Feng Liu   +6 more
doaj   +1 more source

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

Oxygen‐Tunnel Indium Tin Oxide Vertical Channel Transistors with Enhanced Current Density and Reliability for Monolithic 3D Compute‐In‐Memory Systems

Advanced Functional Materials, EarlyView.
Oxygen‐tunnel (OT) indium tin oxide (ITO) vertical channel transistors (VCTs) enable reliable, high‐density gain‐cell memory for monolithic 3D integration. A sandwiched SiN/SiO2/SiN OT stack selectively regulates oxygen transport, suppressing parasitic electrode oxidation while stabilizing channel oxygen vacancies, thereby suppressing carrier injection
Hyeonho Gu, Haksoon Jung, Yongwoo Lee, Hoichang Jeong, Yanfeng Zhao, Heesoo Yang, Minho Park, Hyeonjin Lee, Seunghun Baek, Minju Song, Junghwan Kim, Youngmin Jo, Hyunjin Park, Munhyeon Kim, Jae‐Joon Kim, Kyuho Jason Lee, Byungjo Kim, Jimin Kwon   +17 more
wiley   +1 more source

On Loss Functionals for Physics-Informed Neural Networks for Steady-State Convection-Dominated Convection-Diffusion Problems

Solutions of convection-dominated convection-diffusion problems usually possess layers, which are regions where the solution has a steep gradient.
Frerichs-Mihov, Derk, Henning, Linus, John, Volker   +2 more
core   +1 more source

Enforcing hidden physics in physics-informed neural networks

CoRR
Physics-informed neural networks (PINNs) represent a new paradigm for solving partial differential equations (PDEs) by integrating physical laws into the learning process of neural networks. However, despite their foundational role, the hidden irreversibility implied by the Second Law of Thermodynamics is often neglected during training, leading to ...
Nanxi Chen, Sifan Wang, Rujin Ma, Airong Chen, Chuanjie Cui   +4 more
openaire   +2 more sources

Intrinsic Photoactive Star ZnPc–Poly(glutamate) Nanoplatforms for Multimodal Glioblastoma Therapy and Brain‐Targeted Delivery

Advanced Functional Materials, EarlyView.
An intrinsic photoactive star‐shaped zinc phtalocyanine‐poly(L‐glutamic acid) (ZnPc‐PGA) nanoplatform for multimodal glioblastoma (GBM) therapy and brain‐targeted elivery. A ZnPc‐PGA‐based multifunctional theranostic nanocarrier platform enables image‐guided, multimodal GBM therapy. ZnPc‐PGA nanocarriers support the integration of fluorescence imaging,
Amina Benaicha‐Fernández, Teodora Ranđelović, Ana Armiñán, Víctor Sebastián, Manuel Fuentes, Cristián Huck‐Iriart, Esther Masiá, Paula Carrascosa‐Marco, Pablo Juanes‐Velasco, Snežana Đorđević, Clara Bayona, Ignacio Ochoa, Maria Medel, Inmaculada Conejos‐Sánchez, María J. Vicent   +14 more
wiley   +1 more source