[HTML][HTML] Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: Lessons learned from the United Kingdom national …
The COVID-19 pandemic has put unprecedented pressure on public health resources
around the world. From adversity, opportunities have arisen to measure the state and …
around the world. From adversity, opportunities have arisen to measure the state and …
Machine learning for data-centric epidemic forecasting
The COVID-19 pandemic emphasized the importance of epidemic forecasting for decision
makers in multiple domains, ranging from public health to the economy. Forecasting …
makers in multiple domains, ranging from public health to the economy. Forecasting …
Recommendations for quantitative uncertainty consideration in ecology and evolution
Ecological and evolutionary studies are currently failing to achieve complete and consistent
reporting of model-related uncertainty. We identify three key barriers–a focus on parameter …
reporting of model-related uncertainty. We identify three key barriers–a focus on parameter …
Identifiability and predictability of integer-and fractional-order epidemiological models using physics-informed neural networks
We analyze a plurality of epidemiological models through the lens of physics-informed
neural networks (PINNs) that enable us to identify time-dependent parameters and data …
neural networks (PINNs) that enable us to identify time-dependent parameters and data …
Uncertainty-aware deep co-training for semi-supervised medical image segmentation
Semi-supervised learning has made significant strides in the medical domain since it
alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic …
alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic …
Ensembles are required to handle aleatoric and parametric uncertainty in molecular dynamics simulation
Classical molecular dynamics is a computer simulation technique that is in widespread use
across many areas of science, from physics and chemistry to materials, biology, and …
across many areas of science, from physics and chemistry to materials, biology, and …
[HTML][HTML] Quantifying uncertainties in direct numerical simulations of a turbulent channel flow
Direct numerical simulation (DNS) provides unrivalled levels of detail and accuracy for
simulating turbulent flows. However, like all numerical methods, DNS is subject to …
simulating turbulent flows. However, like all numerical methods, DNS is subject to …
Inaccuracies of deterministic finite-element models of human middle ear revealed by stochastic modelling
A Ebrahimian, H Mohammadi, JJ Rosowski… - Scientific reports, 2023 - nature.com
For over 40 years, finite-element models of the mechanics of the middle ear have been
mostly deterministic in nature. Deterministic models do not take into account the effects of …
mostly deterministic in nature. Deterministic models do not take into account the effects of …
Learning from weather and climate science to prepare for a future pandemic
S Schemm, D Grund, R Knutti… - Proceedings of the …, 2023 - National Acad Sciences
Established pandemic models have yielded mixed results to track and forecast the SARS-
CoV-2 pandemic. To prepare for future outbreaks, the disease-modeling community can …
CoV-2 pandemic. To prepare for future outbreaks, the disease-modeling community can …
Global ranking of the sensitivity of interaction potential contributions within classical molecular dynamics force fields
Uncertainty quantification (UQ) is rapidly becoming a sine qua non for all forms of
computational science out of which actionable outcomes are anticipated. Much of the …
computational science out of which actionable outcomes are anticipated. Much of the …