Results 161 to 170 of about 3,284,626 (292)

Data‐Driven and Physics‐Informed Modeling Perspectives for Soft Manipulators: From SINDy to Hybrid Intelligence

Advanced Science, EarlyView.
This Perspective charts the modeling landscape for soft manipulators, spanning sparse system identification, neural dynamics, physics‐informed models and hybrid intelligence. We examine how physics, data, sensing, adaptation and control can assume complementary roles within this landscape, and provide a framework for selecting modeling approaches ...
Wenbo Hui, Yunyi Wang, Teng Zhang, Yang Chen, Min Pan   +4 more
wiley   +1 more source

Advancing multilevel Bayesian networks with efficient Bayesian inference. [PDF]

Stat Methods Med Res
Yirdaw BE, Debusho LK, van Niekerk J, Rue H.   +3 more
europepmc   +1 more source

Multiscale Regulation of Ru‐Based Catalysts for Upgrading Biomass‐Derived Levulinic Acid and Levulinates to γ‐Valerolactone: Mechanisms, Advances, and Challenges

Advanced Science, EarlyView.
This review summarizes recent advances in the mechanistic, thermodynamic, and kinetic studies of biomass‐derived levulinic acid/levulinates conversion to γ‐valerolactone, along with the corresponding multiscale regulation strategies of Ru‐based catalysts.
Xin Ni, Cailu Xiao, Gongbing Zhou, Jingzi Zhang   +3 more
wiley   +1 more source

Bayesian Networks and Causal Discovery. [PDF]

Entropy (Basel)
Gao X, Wang Z.
europepmc   +1 more source

Bayesian neural network learning for repeat purchase modelling in direct marketing. [PDF]

We focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted.
Van den Poel, D, Vanthienen, Jan, Baesens, Bart, Dedene, Guido, Viaene, Stijn   +4 more
core  

From Artificial to Embodied and Physical Intelligence in Smart Manufacturing

Advanced Science, EarlyView.
Smart manufacturing is reframed through complementary artificial, embodied, and physical intelligence, spanning computational reasoning, perception‐action coupling, and material‐level adaptation. Their integration across design, fabrication, operation, and maintenance reveals how intelligence can be co‐designed across algorithms, embodied agents, and ...
Kang Wang, Xinyu Lu, Tong Liu, Jinjie Fang, Xin Li, Siqian Wu, Liuchao Jin, Wei‐Hsin Liao, Shuyou Zhang, Jianrong Tan   +9 more
wiley   +1 more source

Evaluation of the high-quality development of graduate education in mining engineering based on ISM and Bayesian networks. [PDF]

PLoS One
Wu L, Sun Z, Yang Y.
europepmc   +1 more source

Modeling hierarchical relationships in epidemiological studies: a Bayesian networks approach [PDF]

Hierarchical relationships between risk factors are seldom taken into account in epidemiological studies though some authors stressed the importance of doing so, and proposed a conceptual framework in which each level of the hierarchy is modeled ...
Nguefack-Tsague, Georges, Zucchini, Walter   +1 more
core  

An AI‐Driven Framework for Directed Self‐Assembly Toward Design‐Technology Co‐Optimization

Advanced Science, EarlyView.
An artificial intelligence (AI)‐driven closed‐loop framework integrates experiment, three‐dimensional physics‐based simulation, and template design for directed self‐assembly (DSA). Guided by experimental data, AI‐assisted calibration accelerates model optimization, supports morphology prediction, and identifies template windows.
Caiwei Shang, Yanting Xu, Yue Zhang, Yan Zhang, Jianfeng Li, Ming Li, Longlin Lei, Wenda Bao, Weixuan Zeng, Shisheng Xiong   +9 more
wiley   +1 more source

Dynamic Bayesian networks for neural information flow: evaluation of continuous and discrete scoring metrics. [PDF]

J Comput Neurosci
Thomas-Hegarty J, Pulver SR, Smith VA.
europepmc   +1 more source