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The models solve the equations of fluid dynamics, and they do a very good job of describing the fluid motions of the atmosphere and the oceans.
Thus, the CFD models solve the Navier Stokes equations, and the general transport equations for each physical quantity.
Specifically, such models solve the heat conduction equation in order to estimate the temperature in the regolith as function of time and depth below the surface.
Such models solve the coupled continuity, momentum and energy equations and provide the densities, temperature and velocities of the major species.
These models solve the cardinality problem by restricting the domain X of interpretation, so that, in them, X is in a suitable sense isomorphic to the 'function space' XX.
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The use of the logit and probit models solves the problem of OLS estimation for binary dependent variables.
These models solved the same complex equations of atmospheric physics that numerical weather prediction models did.
This reflects a strength of DCM; the Bayesian inversion of these models solves the complex problem of estimating hidden or latent variables.
How does this business model solve the information problems described above?
By contrast, while the proposed model solved the problems efficiently.
The model solves the tradeoffs between investment and production capacity.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com