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However, correlating million degrees of freedom of numerical models to the few degrees of freedom of test models is challenging.
Obtaining building models is challenging, as buildings' thermal dynamics are nonlinear, have long time delays, and contain uncertainties.
Even if there is not something radically new here regarding scientific explanation, the kind of understanding provided by chaos models is challenging to clarify.
Exploring and understanding outputs from agent-based models is challenging due to a relatively higher number of parameters and multidimensionality of the generated data.
However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements.
Controlling for factors that could influence the study by applying multivariate regression models is challenging given the complexity of stock market data.
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Protecting the sink's location privacy under the global attack model is challenging.
Due to the complex nature of the process involving dyes-coagulant, the modelling is challenging.
Dynamic modelling is challenging when low permeability of the rock matrix creates transient effects that can persist for long periods.
Learning these local features from a boundary representation (B-Rep) CAD model is challenging due to lack of volumetric information.
A designing peptide as agent for inducing diabetes mellitus type 2 (T2DM) in an animal model is challenging.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com