Results 121 to 130 of about 3,102,730 (292)
Empirical evaluation of scoring functions for Bayesian network model selection
BMC Bioinformatics, 2012 In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning ...
Liu Zhifa, Malone Brandon, Yuan Changhe
doaj +1 more source
Comput. Artif. Intell., 1997 Summary: The paper presents a causal-probabilistic approach to the technical diagnosis in which the solution of the technical diagnostic problem is considered as a probabilistic inference on a special kind of Bayesian networks called diagnostic Bayesian networks.
openaire +3 more sources
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 +8 more
wiley +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 +3 more
wiley +1 more source
Inference in Bayesian Networks.
AI Mag., 1999 A Bayesian network is a compact, expressive representation of uncertain relationships among parameters in a domain. In this article, I introduce basic methods for computing with Bayesian networks, starting with the simple idea of summing the probabilities of events of interest. The article introduces major current methods for exact computation, briefly
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Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers
Advanced Science, EarlyView.FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao +9 more
wiley +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 +8 more
wiley +1 more source
Model assisted approaches to complex survey sampling from finite populations using Bayesian Networks [PDF]
A class of estimators based on the dependency structure of a multivariate variable of interest and the survey design is defined. The dependency structure is the one described by the Bayesian networks. This class allows ratio type estimators as a subclass
Paola Vicard, Marco Ballin, Mauro Scanu
core
Applying Bayesian networks to model uncertainty in project scheduling [PDF]
, 2009 PhDRisk Management has become an important part of Project Management. In spite of numerous advances in the field of Project Risk Management (PRM), handling uncertainty in complex projects still remains a challenge.
Khodakarami, Vahid
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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 +12 more
wiley +1 more source