Results 141 to 150 of about 12,151,236 (300)

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

A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing. [PDF]

PLoS Comput Biol, 2023
Patruno L, Milite S, Bergamin R, Calonaci N, D'Onofrio A, Anselmi F, Antoniotti M, Graudenzi A, Caravagna G.   +8 more
europepmc   +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

Bait-ER: A Bayesian method to detect targets of selection in Evolve-and-Resequence experiments. [PDF]

J Evol Biol, 2023
Barata C, Barata C, Borges R, Kosiol C.
europepmc   +1 more source

Bayesian Probability Theory

, 2007
This paper argues that Bayesian probability theory is a general method for machine learning. From two well-founded axioms, the theory is capable of accomplishing learning tasks that are incremental or non-incremental, supervised or unsupervised.
Wei-min Shen, General Method
core  

A Bayesian method for reducing bias in neural representational similarity analysis

, 2016
In neuroscience, the similarity matrix of neural activity patterns in response to different sensory stimuli or under different cognitive states reflects the structure of neural representational space.
Cai, Mingbo, Niv, Yael, Pillow, Jonathan, Schuck, Nicolas   +3 more
core   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

A reliable and efficient adaptive Bayesian method to assess static lower limb position sense. [PDF]

J Neurosci Methods, 2023
Wood JM, Morton SM, Kim HE.
europepmc   +1 more source

An approach to non-linear Bayesian forecasting problems with applications [PDF]

This thesis is devoted to the analysis and modelling of time series and it is concentrated on models and techniques which are of practical value. In particular we developed a wide class of non-linear dynamic models which are useful in the handling of ...
Migon, Helio dos Santos
core  

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source