Results 81 to 90 of about 16,533 (261)

Physics-Informed Deep Neural Operator Networks

, 2023
33 pages, 14 figures.
Somdatta Goswami, Aniruddha Bora, Yue Yu 0011, George Em Karniadakis   +3 more
openaire   +2 more sources

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-level physics informed deep learning for solving partial differential equations in computational structural mechanics

Communications Engineering
Physics-informed neural network has emerged as a promising approach for solving partial differential equations. However, it is still a challenge for the computation of structural mechanics problems since it involves solving higher-order partial ...
Weiwei He, Jinzhao Li, Xuan Kong, Lu Deng   +3 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

Neuromorphic, physics-informed spiking neural network for molecular dynamics

Machine Learning: Science and Technology
Molecular dynamics (MD) simulations are used across many fields from chemical science to engineering. In recent years, Scientific Machine Learning (Sci-ML) in MD attracted significant attention and has become a new direction of scientific research ...
Vuong Van Pham, Temoor Muther, Amirmasoud Kalantari Dahaghi   +2 more
doaj   +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

Two-Scale Physics-Informed Neural Networks for Structural Dynamics Parameter Inversion: Numerical and Experimental Validation on T-Shaped Tower Health Monitoring

Buildings
We present a two-scale physics-informed neural network (TSPINN) algorithm to address structural parameter inversion problems involving small parameters. The algorithm’s core mechanism directly embeds small parameters into the neural network architecture.
Xinpeng Liu, Xuemei Zhang, Yongli Zhong, Zhitao Yan, Yu Hong   +4 more
doaj   +1 more source

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

Physics-Informed Neural Networks

Neural networks have been used extensively in many fields with impressive results.Most applications use data as the sole learning source. Recently, researchers have proposedto use additional information about the latent data that gives birth to information-informedmachine learning.
Georgios E. Stavroulakis, Aliki D. Mouratidou, Georgios A. Drosopoulos   +2 more
openaire   +3 more sources