Results 131 to 140 of about 3,905,952 (289)

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

Advanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen, Chao Xu, Jingyue Yu, Qi Wang, Hongze Fang, Shuo Yin, Turab Lookman, Ruirun Chen   +7 more
wiley   +1 more source

Taming the reservoir : feedforward training for recurrent neural networks

, 2012
Recurrent neural networks are successfully used for tasks like time series processing and system identification. Many of the approaches to train these networks, however, are often regarded as too slow, too complicated, or both.
Obst, Oliver, Obst, Oliver (R18459), Martin Riedmiller, Riedmiller, Martin, Oliver Obst   +4 more
core   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

Signatures of Bayesian inference emerge from energy-efficient synapses

eLife
Biological synaptic transmission is unreliable, and this unreliability likely degrades neural circuit performance. While there are biophysical mechanisms that can increase reliability, for instance by increasing vesicle release probability, these ...
James Malkin, Cian O'Donnell, Conor J Houghton, Laurence Aitchison   +3 more
doaj   +1 more source

Multiscale Circuit Architecture Associated With Memory Dysfunction in Temporal Lobe Epilepsy

Advanced Science, EarlyView.
A multiscale precision‐mapping framework reveals that memory impairment in temporal lobe epilepsy arises from the convergence of focal medial temporal pathology, strategic white matter disconnection, and limbic‐centered metabolic network dysfunction.
Jiajie Mo, Qiwen Yuan, Pengfei Zhang, Lin Sang, Zhong Zheng, Baotian Zhao, Xiu Wang, Chao Zhang, Wenhan Hu, Xiaoqiu Shao, Jianguo Zhang, Ruijin Yang, Kai Zhang   +12 more
wiley   +1 more source

QEKI: A Quantum–Classical Framework for Efficient Bayesian Inversion of PDEs

Entropy
Solving Bayesian inverse problems efficiently stands as a major bottleneck in scientific computing. Although Bayesian Physics-Informed Neural Networks (B-PINNs) have introduced a robust way to quantify uncertainty, the high-dimensional parameter spaces ...
Jiawei Yong, Sihai Tang
doaj   +1 more source

Comparing the performance of different neural networks architectures for the prediction of mineral prospectivity [PDF]

, 2005
In the mining industry, effective use of geographic information systems (CIS) to identify new geographic locations that are favorable for mineral exploration is very important. However, definitive prediction of such location is not an easy task.
Brown, W., Wong, K.W., Warick Brown, Fung, C.C., Iyer, V., Chun Che Fung, Vanaja Iyer, Kok Wai Wong   +7 more
core  

StackingNet: Collective Inference Across Independent AI Foundation Models

Advanced Science, EarlyView.
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding   +4 more
wiley   +1 more source

BAYES-GAIT: A Bayesian Deep Neural Network for Continuous Gait Reference Trajectory Generation Using an Enhanced Loss Function

IEEE Access
Background: Adaptive gait trajectory prediction is essential to achieve natural and stable locomotion in prosthetic limbs and legged robots, particularly under varied conditions such as changing inclines and walking speeds.
Bharat Singh, Suchit Patel, Ankit Vijayvargiya, Rajesh Kumar, Naveen Gehlot   +4 more
doaj   +1 more source

Bayesian Learning Of Neural Networks By Means Of Artificial Immune Systems

, 2015
Once the design of Artificial Neural Networks (ANN) may require the optimization of numerical and structural parameters, bio-inspired algorithms have been successfully applied to accomplish this task, since they are population-based search strategies ...
Von Zuben F.J., Castro P.A.D.
core