Results 51 to 60 of about 1,009,621 (180)

Cohesion: A Measure of Organisation and Epistemic Uncertainty of Incoherent Ensembles

Entropy, 2023
This paper offers a measure of how organised a system is, as defined by self-consistency. Complex dynamics such as tipping points and feedback loops can cause systems with identical initial parameters to vary greatly by their final state.
Timothy Davey
doaj   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Vertical NAND Flash Memory‐Based Bayesian Neural Networks With Intrinsic Gaussian Weight Sampling

Advanced Intelligent Systems, EarlyView.
Commercial vertical NAND flash memory supports Bayesian neural networks by using paired cells to produce bell‐shaped weight distributions. Read voltage controls their spread without additional memory cells or changes to the array structure. Simulations based on device measurements demonstrate improved uncertainty estimation and accuracy gains of up to ...
Sung‐Ho Park, Mingyun Oh, Dongbeen Shin, Jaehyeon Kim, Jonghyun Ko, Jiseong Im, Yeongheon Yang, Gyuweon Jung, Jong‐Ho Lee   +8 more
wiley   +1 more source

Experimental methods in chemical engineering: The scientific method versus engineering design methodology

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Engineering graduate students confuse the scientific method and problem solving when presenting their pre‐doctoral exams and theses: Science discourse adopts question and hypotheses to assert and establish a study's rationale, while it is problem and objectives in engineering design.
Gregory S. Patience, Alexander R. P. Harrison, Nicolas A. Patience, Paul A. Patience, Robert S. Weber   +4 more
wiley   +1 more source

From Biological Mechanisms to Causal Inference: Quantitative Systems Pharmacology and Causal Frameworks in Drug Development

Clinical Pharmacology &Therapeutics, EarlyView.
Digital patients and in silico clinical trials offer new opportunities to advance pharmacological research, clinical development, and regulatory decision‐making by enabling the simulation of treatment effects prior to testing in patients. Among the most promising of these opportunities is the combined use of quantitative systems pharmacology (QSP) and ...
Thomas Klabunde, Ramon Hernandez, Piet H. van der Graaf, Tommaso Andreani   +3 more
wiley   +1 more source

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

Deep Underground Science and Engineering, EarlyView.
Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley   +1 more source

Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

Sensors, 2021
Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to ...
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor   +3 more
doaj   +1 more source

Calibration of medium‐range temperature ensemble forecasts dependent on the time horizon: An assessment

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Numerical weather prediction output necessitates calibration, as weather model systems are unable to replicate the correct statistics of observations. Such processing is especially important when predicting extreme weather. Calibration methods function by learning from prediction inadequacies during a training phase and by exploiting this information ...
Dmitrij Japs, Oleksiy Boyko, Paolo Reggiani, Ezio Todini   +3 more
wiley   +1 more source

Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts

CoRR, 2022
Published in ICLR ...
Zhitong Gao, Yucong Chen, Chuyu Zhang, Xuming He 0001   +3 more
openaire   +3 more sources

Explainable Machine Learning for Predicting Time‐to‐Failure of Horizontal Pressurized Storage Tanks in Fire Scenarios

Safety Science and Technology, EarlyView.
An interpretable machine learning framework with SHAP analysis achieves accurate time‐to‐failure prediction for horizontal pressurized tanks under pool fires while identifying fire intensity, filling level, and wall thickness as dominant drivers of model explanation and uncertainty explanation, providing transparent and actionable insights for ...
Yubin Mu, Mingshu Bi, Zilong Deng, Di Yu, Jingjing Yang, Jingjie Ren   +5 more
wiley   +1 more source