Hi, concerning the modeling and simulation software, you could use Matlab - simulink (commercial) or Scilab - Scicos (freeware). There are also many other SW, like Arena, Simprocess, etc. Try wikipedia to obtain more information. About the scholarly material, try to obtain some lecture notes from the web. Additionally, there also exist many modeling and simulation based books. 

The first question is: what do you actually simulate. There are practically zillions of programs out there for simulating each and every special purpose. Some are more general, and very flexibel, but you need to write some code to adapt them to your purposes. Others are already dedicated for a certain application. Then you only need (and only can) specify some parameters to obtain a result. Less flexibel, but much faster in return. 

I agree with prof. Armin. For example, if you are using Matlab or Scilab, they are quite general and flexible, but probably some (programming and other) efforts would be needed to adapt them into your problem. On the ohter side, there are many less flexible softwares, but the question is if they can exactly fit into your problem by only using some parametrization.

Thank a lot guys for your responses.

As am new to thing domain so don't have i do not have a concrete problem statement but i am thinking of some automobile modelling or Airplane modelling but that is not fixed.

My main concern is can i do mathematical part on my own? I do not have problem about programming  so can i create my own model and use graphical tool for Visualization?  Or is it better to use tools for it?  

I do not have any problem with MATLAB  also.

So please suggest me i am little bit confused.

This really strongly depends on what exactly you want to do - automobile modelling can be anything from stress analysis, crash simulation, air resistance, manufacturing processes, customer comfort or even economic problems.

In general, programming things on your own will give you a better understanding of the basics, but for complex real-world problems, it will be difficult to implement the features that commercial (or existing research) codes have. If publishable research is your goal, you should probably go with existing codes, otherwise you will spend a lot of time re-programming what others have already done.

My advice would be to select as concrete a problem as possible to start with, looking at existing literature to see what tools people are using for this and to define a research question.

As I am familiar with, there exist already developed models for cars, as well as for aircrafts in matlab. Try to look at simulink manual, or go the mathworks file echange. 

The best tool wiil be the one that matches your objective and data

Naturally, I agree that the basic properties of the procees, which is modelled, must be firstly satisfactory understood. Then, the background of modeling expressions and the theoretical working mechanism of the model must be understood. Usually, some assumptions and simplifications are also additionally applied to the model to reduce its complexity. Afterwards, the results of simulations of system dynamic behavior must be logical and the model response has to behave according to our expectations.  

following are the different tools for different research area and applications;

ASCEND - open source equation-based modelling environment.

DWSIM - An open-source CAPE-OPEN compliant chemical process simulator.

Elmer - An open source multiphysical simulation software for Windows/Mac/Linux.

Facsimile - a free, open-source discrete-event simulation library.

Freemat - a free environment for rapid engineering and scientific prototyping and data processing.

Galatea - A multi-agent, multi-programming language, simulation platform.

GNU Octave - An Open Source mathematical modeling and simulation software very similar to MATLAB.

Minsky (economic simulator) - an open source visual computer program for dynamic simulation of economic models.

ns-3 - a popular Open Source network simulator.

Mobility Testbed - an Open Source multi-agent simulation testbed for transport coordination algorithms.

OpenFOAM - an open-source used for Computational Fluid Dynamics (or CFD)

OpenEaagles - multi-platform simulation framework targeted to help simulation engineers and software developers rapidly prototype and build robust, scalable, virtual, constructive, stand-alone, and distributed simulation applications.

Open Source Physics - An expansive, open-source Java software project for teaching and studying physics. It contains packages for drawing and plotting, solving differential equations, exporting to animated GIFs and movies, simulations, and much more.

Physics Abstraction Layer - an open source physics simulation package.

Sage - System for Algebra and Geometry Experimentation via Python.

Scilab - Free Open Source Software for Numerical Computation & Simulation similar to matlab

SimPy - an open-source discrete-event simulation package based on Python.

Stanford University Unstructured - an open-source used for Computational Fluid Dynamics simulation and Optimal Shape Design

Step - an open source two-dimensional physics simulation engine (KDE)

Tortuga - An open source software framework for discrete-event simulation in Java.

Python is also very convenient software for simulation, more flexible and faster than MATLAB. But it is always better to work with FORTRAN or C++ especially to work with large size network.

1) If you have excel on your computer why not teach yourself Visual Basic for Applications (VBA). As most other people also have Excel you will be able to share your models with others and they with you. http://www.anthony-vba.kefra.com/index_011.htm

2) You can learn OCTAVE, which is free and reasonably compatible with Matlab. You can write your own programs and find programs on the net that you can run yourself and get an understanding of programming and how OCTAVE works. There are packages that can be added to OCTAVE for various problems that you might want to solve.

https://www.gnu.org/software/octave/download.html

3) You can learn R which is another programming language which also comes with various packages and is free.

http://cran.r-project.org/

4) If you want to learn or model dynamical systems then you have xppaut.

http://www.math.pitt.edu/~bard/bardware/tut/xpptut.html

5) You can learn to use Genius Calculator which is quick and easy to learn.

http://www.jirka.org/genius.html

1) runs on windows alone as far as i am aware

5) runs on linux alone

2) 3) 4) run on windows and linux

You can use mathbuntu which is a linux operating system which you can run on a USB stick if you do not want to replace your operating system on your drive. It comes with various maths books, above programming languages and others.

http://www.mathbuntu.org/

https://www.youtube.com/watch?v=uWO3vif7hTw

http://www.pendrivelinux.com/put-ubuntu-10-04-on-flash-drive-using-windows/

This should get you started.

Depends on your domain area. If want to work in network domain, you can use MATLAB Communication module, NS2, NS3-Object oriented support, OMNET etc. If your domain is machine learning then many tools are available depending on the statistics and way of calculating the probabilities. 

Dear Prashant,

Another option is Scilab. Scilab is free and open source, which allows us to compute mathematical models. You can download at http://www.scilab.org/

Ebert

http://www.scilab.org/

The choice of tool is really context and content dependent. It pretty much depends on your objectives,  in the modeling and simulation; is speed an issue? Then c++ eg might be you tool of choice, how large is the number of simulations you want to run? Ie order of complexity of the problem. Tools like matlab,  python,  r, perl also perform very well for fairly moderately complex problems. Again,  the definition of how complex is complex depends on what you are working on.

Hey ! Just get hands on with python scripting : (For Following Reasons)

- Open source

- Has loads of freely available packages that does everything to you, just you need them get it installed

- Easy to learn

- Platform independent

- High end scientific computing tool available in the market with fully loaded modern algorithms that can solve any type of problem in any domain

- Gives awesome representations

Easy way to get python is just google : anacoda python free download --- > Move on !!! by installing it 

Some packages i would like you to google : 

scipy

numpy

matplotlib

sympy

os

pylab

tkinter

ipython

All the Best :)

I think any coding language can be used for simulation and modeling , but if you are searching for the best , in my opinion its the one that helps you in your task , it may contain already build up blocks and functions , for now i'm satisfied with matlab / simulink . 

I also agree with Salah Eddine Ghamri. MATLAB is universal software, and anthing can be modeled.

If you are still looking for tools:

Try JavaScript and the Acrobat/JavaScript API. You can draw your graphics using a vector-based software such as PowerPoint and export the graphics to PDF. If you have some sample problems in mind, post it and I will try to come up with computer programs written in JavaScript and using the Acrobat/JavaScript API as examples for you.

Is this the "best" or is this a "good tool" for simulation and modelling? I don't know but it is a tool I will use.

I recommend the following:

SPSS with AMOS

R

Matlab

KH Coder- Open source

Some syllabus or teach experience with Scilab, Octave, Python or Excel?

I think that Individual-based models (IBM) constitute a good tool to model complex systems (in physics, biology, ecology, epidemiology, economics and social sciences). According to the definition given by Craig Reynolds "Individual-based models are simulations based on the global consequences of local interactions of members of a population. These individuals might represent plants and animals in ecosystems, vehicles in traffic, people in crowds, or autonomous characters in animation and games. These models typically consist of an environment or framework in which the interactions occur and some number of individuals defined in terms of their behaviors (procedural rules) and characteristic parameters. In an individual-based model, the characteristics of each individual are tracked through time. This stands in contrast to modeling techniques where the characteristics of the population are averaged together and the model attempts to simulate changes in these averaged characteristics for the whole population. Individual-based models are also known as entity or agent based models, and as individual/entity/agent-based simulations "

Some references about this modelling approach:

1) Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, Goss-Custard J, Grand T, Heinz SK, Huse G, et al (2006); A standard protocol for describing individual-based and agent-based models. Ecological Modelling 198(1):115-126

2) Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF (2010): The odd protocol: a review and first update. Ecological Modelling 221(23):2760-2768

Old thread uh!

I am not giving any answer but to draw the clever questioner Prashant Chavan's attention and those with similar quests that (I say this with a sweeping generalisation) the word "best" in technology doesn't apply in its primary sense. Reason? Almost everything in ICT depends on requirements to solve a problem. Think of hardware, software, operating environment, portability, interpretability, cost, performance ... and the list goes on.

To somewhat get a soluble answer, you need to elaborate more on what you want to achieve in the end, and that will give answerers enough ground to think for you and offer suggestions.

Voila!

Personal opinion, my stand.

I have been recently simulating queue networks under Simmer. Simmer is a R package, which has been developed by Iñaki Ucar & Bart Smeets, and it is very useful for modelling discrete event dynamic systems, such as queue networks.

For getting information about Simmer https://r-simmer.org/ , and also for looking an example of a very simple five queue network, http://rpubs.com/ebrea/simulation5QN.

Ebert

Dear Prashant,

To do mathematical modelling you can use the below software.

To do the simulation using exact approach, please use either GLPK, or LINGO

To do the simulation using heuristics or meta-heuristics, please use either MATLAB, or R.

Also, if you want to do statistical modelling, then you can use SPSS, AMOS, and R.

It depends on the problem you are trying to solve. The simulation method will determine the tool. In the industry the most common tools are AnyLogic and Simul8. They both have download tutorials to help beginers. These tools come with library of resouces to get learners started.

I believe it is depends on the area or the topic that you want to simulate

For example, if you want to simulate core-network protocols mostly NS2 is better.

For wireless sensor networks, NS2, NS3, OmNet++

And there is no general simulator that is the best for all areas.

I used MATLAB. Its toolboxes and open-source files on the file-exchange server are really very helpful.

Matlab

Shadow (Discrete Event Simulator)

an open-source network simulator/emulator hybrid runs real applications like Tor and Bitcoin over a simulated Internet topology. It is light-weight, efficient, scalable, parallelized, controllable, deterministic, accurate, modular, and more!

http://shadow.github.io/

Much chemical process modelling and/or simulation work can be done with the widely-used Microsoft’s Excel.

Application example ― Simulations carried in Excel 5.0 with Visual Basic for Applications (VBA) macros ― The recursive least squares algorithm (RLS) allows for (real-time) dynamical application of least squares regression to time series. Years ago, while investigating adaptive control and energetic optimization of aerobic fermenters, I have applied the RLS algorithm with forgetting factor (RLS-FF) to estimate the parameters from the KLa correlation, used to predict the O2 gas-liquid mass-transfer, while giving increased weight to most recent data. Estimates were improved by imposing sinusoidal disturbance to air flow and agitation speed (manipulated variables). The power dissipated by agitation was accessed by a torque meter. The proposed (adaptive) control algorithm compared favourably with PID. This investigation was reported at (MSc Thesis):

Thesis Controlo do Oxigénio Dissolvido em Fermentadores para Minimi...