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The problem is considered on the juxtaposition of two intervals, one of which of small length δ, and models physical phenomena in media constituted by two parts with different physical characteristics.
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At its heart is the notion of modelling physical phenomena in terms of simple computer programs, rather than complicated mathematical equations.
Modeled physical phenomena included momentum exchange and turbulence due to both shear and slip among phases.
Lévy processes can be used to model physical phenomena that feature jumps.
In the recent decade, the study of nonlinear partial differential equations in modelling physical phenomena has become an important tool.
When one solves PDEs modelling physical phenomena, it is of great importance to take physical constraints into account.
The Caputo fractional derivative is commonly used in modeling physical phenomena but it is possible to assign a physical interpretation meaning for the Riemann-Liouville fractional derivative too.
How about using some of the 3D immersive capabilities provided by the Oculus SDK to simulate the power of probability in simulations or to model physical phenomena with calculus?
Here, we propose a new approach to model physical phenomena and track their evolution by taking advantage of the recent developments of pattern recognition for nonlinear functional learning.
The first paper, "A decentralized approach for nonlinear prediction of time series data in sensor networks" by Honeine et al., proposes a new approach to model physical phenomena and track their evolution with nonlinear functional learning.
The differential equations and variational problems with p ( x ) -growth conditions have been much interest in recent years since they can model physical phenomena which arise in the study of elastic mechanics, electro-rheological fluid dynamics and image processing, etc.
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