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GPkit is a Python package for cleanly defining and manipulating geometric programming models. There are a number of example GP models written with this package here . GGPLAB is a MATLAB toolbox for specifying and solving geometric programs (GPs) and generalized geometric programs (GGPs).
It is easily measured empirically and can be used to extract certain channels' parameters such as the delay spread. For Small Scale channel modeling, the power delay profile of the channel is found by taking the spatial average of the channel's baseband impulse response i.e. | h b ( t , τ ) | 2 {\displaystyle |h_{b}(t,\tau )|^{2}} over a local ...
CadQuery uses Python to generate CAD models and is based on the OpenCascade framework. It is released under the Apache 2.0 Open-Source License . PicoGK is an open-source framework for Computational Engineering which was released under the Apache 2.0 Open-Source License in 2023 by LEAP 71, a Dubai-based company.
JAX is a machine learning framework for transforming numerical functions. [ 2 ] [ 3 ] [ 4 ] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).
The principle of parametric design can be defined as mathematical design, where the relationship between the design elements is shown as parameters which could be reformulated to generate complex geometries, these geometries are based on the elements’ parameters, by changing these parameters; new shapes are created simultaneously.
Generally, the minimum number of parameters required to describe a model or geometric object is equal to its dimension, and the scope of the parameters—within their allowed ranges—is the parameter space. Though a good set of parameters permits identification of every point in the object space, it may be that, for a given parametrization ...
The discipline of stochastic geometry entails the mathematical study of random objects defined on some (often Euclidean) space.In the context of wireless networks, the random objects are usually simple points (which may represent the locations of network nodes such as receivers and transmitters) or shapes (for example, the coverage area of a transmitter) and the Euclidean space is either 3 ...
Geometric constraint solving is constraint satisfaction in a computational geometry setting, which has primary applications in computer aided design. [1] A problem to be solved consists of a given set of geometric elements and a description of geometric constraints between the elements, which could be non-parametric (tangency, horizontality, coaxiality, etc) or parametric (like distance, angle ...