Results 101 to 110 of about 224,776 (267)

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source

Bayesian Sparsification for Deep Neural Networks With Bayesian Model Reduction

IEEE Access
Deep learning’s immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques.
Dimitrije Markovic, Karl J. Friston, Stefan J. Kiebel   +2 more
doaj   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +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

Physics-Prior Bayesian Neural Networks in Semiconductor Processing

IEEE Access, 2019
With the fast scaling-down and evolution of integrated circuit (IC) manufacturing technology, the fabrication process becomes highly complex, and the experimental cost of the processes is significantly elevated.
Chun Han Chen, Parag Parashar, Chandni Akbar, Sze Ming Fu, Ming-Ying Syu, Albert Lin   +5 more
doaj   +1 more source

Customizing Tactile Sensors via Machine Learning‐Driven Inverse Design

Advanced Science, EarlyView.
ABSTRACT Replicating the sophisticated sense of touch in artificial systems requires tactile sensors with precisely tailored properties. However, manually navigating the complex microstructure‐property relationship results in inefficient and suboptimal designs.
Baocheng Wang, Depeng Kong, Zhiao He, Jikai Liang, Yuyao Lu, Zikang Deng, Honghe Li, Mengke Wang, M. Jamal Deen, Zhiqiu Ye, Shuyao Zhou, Huayong Yang, Honghao Lyu, Jun Chen, Kaichen Xu, Geng Yang   +15 more
wiley   +1 more source

A smart headband for multimodal physiological monitoring in human exercises

Advanced Science, EarlyView.
A novel smart headband incorporating a thermal‐sensation‐based electronic skin is presented for continuous and accurate multimodal physiological monitoring, including pulse waveforms, total metabolic energy expenditure, heart rate, and forehead temperature, across both static and dynamic daily activities.
Shiqiang Liu, Guanxing Chen, Jingyi Peng, Shuo Tian, Qian Mao, Caise Wei, Jinfeng Yuan, Rong Zhu   +7 more
wiley   +1 more source

Accurate surrogate amplitudes with calibrated uncertainties

SciPost Physics Core
Neural networks for LHC physics have to be accurate, reliable, and controlled. Using neural surrogates for the prediction of loop amplitudes as a use case, we first show how activation functions are systematically tested with Kolmogorov-Arnold Networks ...
Henning Bahl, Nina Elmer, Luigi Favaro, Manuel Haußmann, Tilman Plehn, Ramon Winterhalder
doaj   +1 more source

Bayesian neural networks for macroeconomic analysis

Journal of Econometrics
JEL: C11, C30, C45, C53, E3, E44.
Hauzenberger, Niko, Huber, Florian, Klieber, Karin, Marcellino, Massimilano   +3 more
openaire   +4 more sources

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