Mathematical Modeling
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RISE Visual Modeling v.1.0
RISE is a modeling tool and code machine available inside Visual Studio 2010. It allows you to graphically create a system model as part of your Visual Studio project and to transform this model directly into source code and database scripts.
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Mathematical fights v.1.0.0.0
Mathematical fights is the cultivate game for children which will help them learn math in a fun playful
Eclipse XML Meta-Modeling Tools v.0.0.1
The Eclipse XML Meta-Modeling Tools project provides tools like editors, extended validation and generators for developing xml based meta-models and domain-specific languages in eclipse.
Generic Eclipse Modeling System v.1.0
The Generic Eclipse Modeling System is an MDD tool for Eclipse that allows for the rapid development of GEF modeling plugins.
Java Modeling Language (JML) v.1.0
The Java Modeling Language (JML) is a behavioral interface specification language that can be used to specify the behavior of Java modules (as in design by contract -- DBC).
Object-Role Modeling v.1.0
The Object-Role Modeling (ORM) standard version 2, associated schemas and generation tools, and a reference implementation in the form of the Natural Object-Role Modeling Architect for Visual Studio (NORMA) product.
PySlice, parametric modeling controller v.1.6.4
Pyslice provides utility functions for parametric modeling.
The little b modular modeling language v.rc
Little b is a Lisp-based language which allows scientists to build shareable, reusable mathematical models of complex systems based on shared parts.
MeshMagic 3D Modeling Software Free v.1.10
With MeshMagic 3D, viewing and editing stl files has never been easier. Import STL files created with CAD and drafting software. You can then use MeshMagic to rotate and rescale the existing meshes.
Calculate Mathematical Expressions Software
Evaluate math expressions with sin, square root, pi, factorial, etc.
Java Neural Modeling Framework new GUI v.1.0
Program to performing the complete cycle of neural networks analysis: preparing data, choosing neural network (CasCor, MP, LogRegression, PNN), learning of network, monitoring learning state, ROC-analysis, optimization of network parameters using GA.