Results 51 to 60 of about 1,952 (235)

Ferroelectric Devices for In‐Memory and In‐Sensor Computing

Advanced Science, EarlyView.
Inspired by biological systems, in‐memory and in‐sensor computing overcome von Neumann bottlenecks. Ferroelectric devices can mimic synaptic functions and sense stimuli like light or force, therefore are ideal for these paradigms. This review introduces the ferroelectric devices applied for in‐memory and in‐sensor computing, covering their structures ...
Hong Fang, Ronghuan Xie, Qiang Cao, Shishen Yan, Xiaolin Wang, Limei Zheng   +5 more
wiley   +1 more source

Stochastic Optimization of Economic Dispatch With Wind and Photovoltaic Energy Using the Nested Sparse Grid-Based Stochastic Collocation Method

IEEE Access, 2019
Due to the increasing uncertainty brought about by renewable energy, conventional deterministic dispatch approaches have not been very applicative. This paper investigates a nested sparse grid-based stochastic collocation method (NS-SCM) as a possible ...
Zhilin Lu, Mingbo Liu, Wentian Lu, Zhuoming Deng   +3 more
doaj   +1 more source

Stochastic Optimal Power Flow for Power Systems Considering Wind Farms Based on the Stochastic Collocation Method

IEEE Access, 2022
The investigation of stochastic optimal power flow (SOPF) is to seek the optimal solution of static stability constrained optimal power flow considering the uncertainty of parameters in power systems. To solve the problem, this paper proposes an approach
Bingqing Xia, Yuwei Chen, Wenbin Yang, Qing Chen, Xiaohe Wang, Kuan Min   +5 more
doaj   +1 more source

Current Challenges of Transcription Compartmentalization Research

Advanced Science, EarlyView.
Transcription factors, coactivators, and RNA polymerase II assemble into transcription compartments ranging from small, defined complexes to liquid‐like condensates. This review unifies these seemingly competing descriptions along a single continuum and asks what these compartments have been shown to do, and what they have not, revealing that the most ...
Thomas Quail, Sina Wittmann
wiley   +1 more source

Computing continuous-time growth models with boundary conditions via wavelets [PDF]

, 2004
This paper presents an algorithm for approximating the solution of deterministic/stochastic continuous-time growth models based on the Euler's equation and the transversality conditions.
Mercedes Esteban-bravo, Jose M. Vidal-sanz, Vidal-Sanz, Jose M., Esteban-Bravo, Mercedes   +3 more
core  

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Sensitivity analysis to compute advanced stochastic problems in uncertain and complex electromagnetic environments

Advanced Electromagnetics, 2012
This paper deals with the advanced integration of uncertainties in electromagnetic interferences (EMI) and electromagnetic compatibility (EMC) problems.
S. Lalléchère, B. Jannet, P. Bonnet, F. Paladian   +3 more
doaj   +1 more source

Stochastic collocation method for computing eigenspaces of parameter-dependent operators

Numerische Mathematik, 2022
AbstractWe consider computing eigenspaces of an elliptic self-adjoint operator depending on a countable number of parameters in an affine fashion. The eigenspaces of interest are assumed to be isolated in the sense that the corresponding eigenvalues are separated from the rest of the spectrum for all values of the parameters.
Luka Grubisic, Mikael Saarikangas, Harri Hakula   +2 more
openaire   +4 more sources

Learning Rocking Dynamics From Sparse Shake‐Table Data With Interpretable Physics‐Informed Neural Networks

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT We present a hybrid interpretable Physics‐Informed Neural Network Long‐Short Term Memory (Hybrid PINN LSTM) framework for predicting the seismic response of rocking blocks. Existing analytical models rely on uncertain idealizations, while purely data‐driven and machine‐learning approaches lack physical consistency and interpretability.
Shirley Shen, Christian Málaga‐Chuquitaype   +1 more
wiley   +1 more source

Projection schemes for stochastic partial differential equations [PDF]

, 2009
The focus of the present work is to develop stochastic reduced basis methods (SRBMs) for solving partial differential equations (PDEs) defined on random domains and nonlinear stochastic PDEs (SPDEs).
Prerapa, Surya Mohan
core