Results 61 to 70 of about 1,009,621 (180)

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

Advanced Intelligent Systems
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, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua   +4 more
doaj   +1 more source

Performance of Conformal Prediction in Capturing Aleatoric Uncertainty

2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Accepted at the IEEE/CVF Winter Conference on Applications of Computer Vision, WACV ...
Misgina Tsighe Hagos, Claes Lundström
openaire   +3 more sources

The evaluation crisis in artificial intelligence‐driven computational biology: Pitfalls and solutions

Quantitative Biology, Volume 14, Issue 4, December 2026.
Abstract Artificial intelligence (AI) is rapidly transforming computational biology. However, closer scrutiny reveals that many reported results do not hold under rigorous evaluation. This paper identifies prevalent, recurring issues in the field, including narrow datasets, hidden confounders that make supposedly independent test data effectively ...
Tsung Fei Khang, Joshua W. K. Ho, Lin Hou, Jiangning Song, Limsoon Wong   +4 more
wiley   +1 more source

Variational aleatoric uncertainty calibration in neural regression [PDF]

, 2021
Des mesures de confiance calibrées et fiables sont un prérequis pour la plupart des systèmes de perception robotique car elles sont nécessaires aux modules de fusion de capteurs et de planification qui interviennent plus en aval.
Bhatt, Dhaivat
core   +1 more source

Field-Level Uncertainty Quantification for AI-Based Ship Hull Surface Pressure Prediction

Journal of Marine Science and Engineering
This study investigates uncertainty quantification for field-level ship hull surface pressure predictions using a U-Net-based data-driven model. A speed-conditioned U-Net is trained on a large CFD dataset covering multiple ship types and velocity ...
Jeongbeom Seo, Inwon Lee
doaj   +1 more source

Clinical evaluation and regression test of a commercial deep‐learning auto‐segmentation model

Journal of Applied Clinical Medical Physics, Volume 27, Issue 10, October 2026.
Abstract Background Commercial deep‐learning segmentation (DLS) tools are increasingly used in clinical practice. Software updates may alter segmentation performance, highlighting the need for systematic clinical evaluation before implementation. Purpose This study presents our experience in clinically evaluating and regression‐testing a RayStation DLS
Roya Barati, Jingwei Duan, Song Gao, Yao Zhao, Xinru Chen, Cenji Yu, Zhan Xu, Moghadaseh Khaleghibizaki, Fredrik Lofman, Jared Ohrt, Laurence Court, Peter Balter, Jinzhong Yang   +12 more
wiley   +1 more source

Automation and Active Learning for the Multi‐Objective Optimization of Antibody Formulations

Advanced Science, Volume 13, Issue 55, 5 October 2026.
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, Matthew Tamasi, Elena Di Mare, Adam J. Gormley   +3 more
wiley   +1 more source

Physics‐encoded transfer learning for scale‐up modeling of CHO cell bioreactors

AIChE Journal, Volume 72, Issue 10, October 2026.
Abstract Developing reliable predictive models for mammalian cell bioreactors, particularly Chinese hamster ovary (CHO) cultures widely used in biopharmaceutical manufacturing, remains challenging due to severe data scarcity in industrial‐scale reactors.
Muyang Li, Ming Xiao, Zhe Wu
wiley   +1 more source

An ensemble learning framework for streamflow reconstruction incorporating aleatoric and epistemic uncertainty

Journal of Hydrology: Regional Studies
Study Region: South Sulawesi, Indonesia Study Focus: Machine learning (ML) models have been utilized to reconstruct streamflow data over data-sparse regions, but their aleatoric and epistemic uncertainty have been limitedly assessed. To address this gap,
Devi Icha Ludyawati, Byeong-Hee Kim, Jonghun Kam   +2 more
doaj   +1 more source

Taguchi–Bayesian Sampling: A Roadmap for Polymer Database Construction Toward Small Representative Machine Learning

Advanced Intelligent Discovery, Volume 2, Issue 5, October 2026.
This article establishes a Taguchi–Bayesian sampling strategy to reconstruct polymer processing–property landscape at minimal sampling cost, generically building the roadmap for materials database construction from sampling their vast design space. This sampling strategy is featured by an alternating lesson between uniformity and representativeness ...
Han Liu, Liantang Li
wiley   +1 more source