Results 131 to 140 of about 3,103,450 (291)
, 2012 In this article, we consider the problem faced by a sensor network operator who must infer, in real time, the value of some environmental parameter that is being monitored at discrete points in space and time by a sensor network.
Osborne, Michael A. +8 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 +3 more
wiley +1 more source
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 +4 more
wiley +1 more source
Advanced Science, EarlyView.To accelerate the inverse design of heterostructured metal matrix composites, a closed‐loop scientific machine learning framework integrates continual learning prediction with NSGA‐II‐PMCP optimization. The framework maps microstructural descriptors to strength, toughness, and modulus, expands high‐quality Pareto solutions, and guides experimentally ...
Zhiyan Zhong +11 more
wiley +1 more source
, 2017 Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relationships among random variables, but they also allow for (potentially) fewer parameters to estimate, and enable more efficient inference.
openaire +2 more sources
Learning Bayesian Networks with the bnlearn R Package [PDF]
bnlearn is an R package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables.
Marco Scutari
core
Bayesian Networks and the Problem of Unreliable Instruments [PDF]
, 2000 We appeal to the theory of Bayesian Networks to model different strategies for obtaining confirmation for a hypothesis from experimental test results provided by less than fully reliable instruments.
Bovens, Luc, Hartmann, Stephan
core
Improved risk analysis for large projects: Bayesian networks approach [PDF]
, 2010 PhDGenerally risk is seen as an abstract concept which is difficult to measure. In this thesis, we consider quantification in the broader sense by measuring risk in the context of large projects.
Fineman, Milijana
core +1 more source
Automation and Active Learning for the Multi‐Objective Optimization of Antibody Formulations
Advanced Science, EarlyView.Successful antibody formulation necessitates balancing factors such as thermal stability, colloidal stability, and viscosity across a vast excipient design space. This work integrates robotic liquid handling, high‐throughput biophysical characterization, and multi‐objective Bayesian optimization in an iterative closed‐loop Design‐Build‐Test‐Learn cycle.
D. Christopher Radford +3 more
wiley +1 more source
Spatial Bayesian neural networks
Spatial Statistics35 pages, 21 ...
Andrew Zammit-Mangion +4 more
openaire +4 more sources