Results 81 to 90 of about 1,009,621 (180)

A Practical Tutorial on Physics‐Informed Networks for Pharmacometrics and Quantitative Systems Pharmacology

CPT: Pharmacometrics &Systems Pharmacology, Volume 15, Issue 10, October 2026.
ABSTRACT Inverse problems in pharmacometrics and quantitative systems pharmacology (QSP) often involve sparse, noisy data, limited measurable states, and complex dynamical systems. Traditional parameter estimation methods can struggle with ill‐posed problems, stiffness, discontinuities, and gray‐box scenarios where only part of the system dynamics is ...
Nazanin Ahmadi Daryakenari, Mohammad Kohandel   +1 more
wiley   +1 more source

Balancing the Unknown: Exploring Human Reliance on AI Advice Under Aleatoric and Epistemic Uncertainty [PDF]

Artificial intelligence systems increasingly support decision-making across a broad range of domains. The complexity of real-world tasks, however, introduces uncertainty into the prediction capabilities of these systems.
Böcking, Lars, Satzger, Gerhard, Holstein, Joshua, Kühl, Niklas, Vössing, Michael, Spitzer, Philipp   +5 more
core   +1 more source

MA-UQNet: A multi-modal uncertainty quantification neural network for remote sensing-based wheat aboveground biomass estimation

Artificial Intelligence in Agriculture
Accurate aboveground biomass estimation with quantified uncertainty is essential for precision agriculture, enabling risk-aware decision-making and strategic model improvement. Existing approaches predominantly provide point estimates without uncertainty
Qiang Wu, Jiao Wang, Yuke Du, Dingyi Hou, Xiaoyu Cao, Xiaochun Wang, Hao Yang, Guijun Yang, Xinming Ma, Jinpeng Cheng   +9 more
doaj   +1 more source

Reliable Intelligent Diagnostics With Uncertainty Quantification for Mechanical System Condition Assessment

Quality and Reliability Engineering International, Volume 42, Issue 6, Page 2896-2916, October 2026.
ABSTRACT Health condition assessment of mechanical systems is essential to ensure their safe and reliable operation. Although AI‐driven diagnostic techniques have advanced significantly with the development of deep learning, the reliability of such techniques is often compromised due to the lack of uncertainty quantification (UQ)—particularly the ...
Guangjun Jiang, Haotian Ouyang, Zifei Xu
wiley   +1 more source

Neural Surrogate HMC: On Using Neural Likelihoods for Hamiltonian Monte Carlo in Simulation‐Based Inference

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Bayesian inference methods such as Markov Chain Monte Carlo (MCMC) typically require repeated computations of the likelihood function, but in some scenarios, this is infeasible and alternative methods are needed. Simulation‐based inference methods address this problem by using machine learning to amortize computations.
L. M. Wolniewicz, P. Sadowski, C. Corti
wiley   +1 more source

A Trustworthy Model With Uncertainty Management for Predicting Vascular Access Dysfunction in Hemodialysis Patients

IEEE Access
Vascular access dysfunction is a prevalent and critical complication among hemodialysis patients, particularly in Taiwan, which has the highest proportion of dialysis patients globally. Early diagnosis and effective management are essential for improving
Chia-Hsun Lin, Zheng Lin Chen, Chung-Kuan Wu, Tsen-Che Wu, Ching-Wen Ma   +4 more
doaj   +1 more source

Site‐Specific Post‐Processing of Spectral Wave Forecast by Learning From Buoy Measurements

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Operational wave prediction systems provide critical information across a range of marine industries and for public safety. Yet operational numerical wave forecasts can exhibit systematic biases, such as in wave height and arrival time of swell events.
Arthur Filoche, Jeff Hansen, Kevin Vinsen, Travis Dawson   +3 more
wiley   +1 more source

Informing synthetic passive microwave predictions through Bayesian deep learning with uncertainty decomposition

Environmental Data Science
Space-borne passive microwave (PMW) data provide rich information on atmospheric state, including cloud structure and underlying surface properties. However, PMW data are sparse and limited due to low Earth orbit collection, resulting in coarse Earth ...
Pedro Ortiz, Eleanor Casas, Marko Orescanin, Scott W. Powell, Veljko Petkovic   +4 more
doaj   +1 more source

Capturing Aleatoric Uncertainty in Climate Models

Environmetrics
ABSTRACT Internal climate variability arises from the climate system's inherently chaotic dynamics. Quantifying it is essential for climate science, as it enables risk‐based decision‐making and differentiates between externally forced change and internal fluctuations.
Cornelia Gruber, Henri Funk, Magdalena Mittermeier, Helmut Küchenhoff, Göran Kauermann   +4 more
openaire   +2 more sources

How Much Physics Should a Neural Network Know? Thermodynamics‐Informed Neural Networks for Rock Constitutive Modeling With Epistemic Uncertainty

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Machine learning offers a flexible route to constitutive modeling, with two emergent questions for geoscience applications: what level of thermodynamic constraint should be embedded in the network architecture, and how should predictive uncertainty be quantified when extrapolating from laboratory to field conditions? We address both challenges
Kangan Li, Tushar Mittal, Benjamin K. Holtzman, Seth Saltiel   +3 more
wiley   +1 more source