Results 81 to 90 of about 14,505 (295)
Frontiers in CommunicationThis study examines epistemic stance markers (ESM) in Corporate Social Responsibility (CSR) reports, focusing on how energy companies manage uncertainty and establish credibility in sustainability communications.
Shuai Liu
doaj +1 more source
Generative modelling under epistemic uncertainty [PDF]
The deployment of Deep Learning in safety-critical domains is hindered by pathological overconfidence and an inability to distinguish between aleatoric uncertainty (data ambiguity) and epistemic uncertainty (lack of knowledge).
Mubashar, Muhammad
core +1 more source
Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy
Advanced Intelligent Discovery, EarlyView.Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang +9 more
wiley +1 more source
A Cognitive Framework for Analysis and Treatment of Uncertainty in Prognostics
Chemical Engineering Transactions, 2013 Uncertainties exist in fault prognostics systems can lead to inaccurate results and this will lead to unnecessary or delay maintenance activities.
B. Sun, T. Liu, S. Liu, Q. Feng
doaj +1 more source
Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction
Advanced Intelligent Discovery, EarlyView.Multimodal fusion integrates composition, crystal‐structure, and radial‐distribution descriptors to predict battery discharge capacity. Rashomon analysis across near‐optimal models reveals that explanatory variation is structured rather than arbitrary, separating stable mechanistic signals from model‐contingent attributions and providing a more ...
Jue Gong +4 more
wiley +1 more source
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? [PDF]
ATrustworthy ML systems should not only return accurate predictions, but also a reliable representation of their uncertainty. Bayesian methods are commonly used to quantify both aleatoric and epistemic uncertainty, but alternative approaches, such as ...
Meinert, Nis +4 more
core +6 more sources
Modelling Epistemic Uncertainty Within Agent-based Models [PDF]
Epistemic uncertainty is largely ignored in agent-based models due to the difficulty of implementation in complex simulations. Some believe introducing epistemic uncertainty provides limited benefits and argue that the same outcome can be achieved by ...
Stepanov, Vladimir
core +1 more source
Advanced Intelligent Discovery, EarlyView.Artificial intelligence is redefining network pharmacology (NP). By integrating knowledge graph engineering, geometric deep learning, multiomics anchoring, and generative reasoning, AI‐driven NP (AI‐NP) transforms static target mapping into dynamic, predictive modeling.
Cong Wang +9 more
wiley +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez +4 more
wiley +1 more source