Results 61 to 70 of about 30,985 (264)

Causal‐Guided Ultra‐Long‐Term Time Series Forecasting Via Anticipated Covariates

Advanced Science, EarlyView.
Often treated as unknown, information from the future remains underutilized.We demonstrate that in a coupled dynamical system, providing the future state of the effect enables accurate forecasting of the cause for a long timesteps. A time series forecasting paradigm that introduces anticipated covariates to represent such known future states is ...
Jintong Zhao, Yufei Liu, Ruixi Huang, Zhongxue Gan, Siyang Leng   +4 more
wiley   +1 more source

Neural network analysis in time series forecasting

Российский технологический журнал
Objectives. To build neural network models of time series (LSTM, GRU, RNN) and compare the results of forecasting with their mutual help and the results of standard models (ARIMA, ETS), in order to ascertain in which cases a certain group of models ...
B. Pashshoev, D. A. Petrusevich
doaj   +1 more source

Halide Perovskite: A Rich Source of Thermal Insulator

Advanced Science, EarlyView.
Halide perovskites exhibit ultralow thermal conductivity driven by intrinsic lattice softness and strong anharmonicity, falling below conventional defect‐engineering limits. Weak metavalent bonding, A‐site rattling, and dynamic octahedral tilting drive phonon scattering to the Ioffe–Regel limit, where wave‐like tunneling replaces particle‐like ...
Haolin Ye, Bangzhi Ge, Yuan Yu, Chongjian Zhou   +3 more
wiley   +1 more source

Hourly concentration prediction of PM2.5 based on RNN-CNN ensemble deep learning model(基于RNN-CNN 集成深度学习模型的PM2.5小时浓度预测)

Zhejiang Daxue xuebao. Lixue ban, 2019
针对目前大部分PM2.5 预测模型预测效果不稳定、泛化能力不强的现状，以记忆能力较强的循环神经网络（RNN） 和特征表达能力较强的卷积神经网络（CNN） 为基础，采取Stacking 集成策略对两者进行融合，提出了RNN-CNN 集成深度学习预测模型。该模型不仅充分利用时间轴上的前后关联信息去预测未来的浓度，而且在不同层次上将自动提取的高维时序数据通用特征用于预测，以保证预测结果的稳定性。最后，对集成之前的 RNN、CNN 和集成之后的RNN-CNN 模型，以2016 年中国大陆地区1 466 ...
HUANGJie(黄婕), ZHANGFeng(张丰), DUZhenhong(杜震洪), LIURenyi(刘仁义), CAOXiaopei(曹晓裴)   +4 more
doaj   +1 more source

Enhanced High Dimensionality and the Information Processing Capacity in Interfered Spin Wave‐Based Reservoir Computing, Achieved With Eight Detectors

Advanced Electronic Materials, EarlyView.
Physical reservoir computing (PRC) based on spin wave interference has demonstrated high computational performance, yet room for improvement remains. In this study, we fabricated this concept PRC with eight detectors and evaluated the impact of the number of detectors using a chaotic time series prediction task.
Sota Hikasa, Wataru Namiki, Daiki Nishioka, Maki Nishimura, Ryo Iguchi, Kazuya Terabe, Takashi Tsuchiya   +6 more
wiley   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

A Dual‐Memory Ferroelectric Transistor Emulating Synaptic Metaplasticity for High‐Speed Reservoir Computing

Advanced Electronic Materials, EarlyView.
A CMOS‐compatible ferroelectric transistor harnesses the interplay between stable gate polarization memory and volatile non‐quasi‐static channel charge dynamics to emulate how biological synapses regulate their own plasticity. This brain‐inspired dual‐memory mechanism, realized in a single device, enables a physical reservoir computer that solves ...
Yifan Wang, Muhammad Sakib Shahriar, Salma Soliman, Noah Vaillancourt, Lance Fernandes, Andrea Padovani, Asif Islam Khan, Md Sakib Hasan, Raisul Islam   +8 more
wiley   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

A Solution for Exosome‐Based Analysis: Surface‐Enhanced Raman Spectroscopy and Artificial Intelligence

Advanced Intelligent Discovery, EarlyView.
Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz, Zakarya Al‐Shaebi, Omer Aydin   +2 more
wiley   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source