Taketechease.com website provides free scientific software for three important mathematical and numerical methods: the Finite Element Method (FEM), Genetic Algorithms (GA) and Machine Learning. Scientific programs can be seen as a really helpful tool in research allowing to find an answer to complex problems where analytic calculations occurred to be not sufficient and where no other solution than numerical approximation is possible to be obtained. This is why this part of work seems to be full of future practical applications in various fields of mathematics, physics and life science. It is worth mentioning that numerics does not exist out of analytic background. Thus, scientific software combines strong analytic calculations with skill of efficient programming.
At that moment, the following software will be proposed to you:
Software for various differential equations based on the Finite Element Method (FEM). Both boundary value and initial value differential equations are considered. The proposed solvers make use of meshes (sets of grid points) that serve as standard discrete patterns for the Finite Element Method. Appropriate meshes together with the FEM approach constitute an effective tool to deal with differential problems. Apart from free 1D fem programs written in Octave and Matlab programming languages you can test two dimensional fem software named Metfem2D. This numerical package is written in Java and has nice user interface that makes it easy in use. To learn more go to the Metfem2d's page.
Software for computer simulations of genetic populations is named OptFinder. This software is written in Java and has nice user interface that makes it easy in use. To learn more go to the OptFinder's page.
To learn more about genetic algorithms go to genetic algorithms page. It will give you necessary background to study e.g. genetic-based machine learning systems.
Software for computer simulations of learning systems is named OptFinderML. This software is written in Java and has nice user interface that makes it easy in use. To learn more go to the OptFinderML's page.
To learn more about machine learning go to machine learning page.