1. Identify your needs: Determine the specific requirements of your synthetic data generation. Consider factors such as the type of data, complexity, desired features, and any specific domain knowledge or algorithms required.

2. Research existing tools: Look for existing simulation tools that align with your needs. Explore open-source software, commercial solutions, or research-oriented tools in your specific domain. Check their features, documentation, user reviews, and community support.

3. Evaluate available options: Compare different simulation tools based on factors like functionality, ease of use, flexibility, scalability, performance, and compatibility with your data format or analysis pipeline. Consider the tool's ability to generate data that closely resembles your target distribution.

4. Consider programming languages: Determine if the simulation tool supports a programming language you are comfortable using or if it provides a user-friendly interface. Some tools may have libraries or APIs that allow integration with your existing codebase.

5. Access and installation: If you find a suitable simulation tool, visit its official website or repository to access the software. Follow the provided installation instructions to set up the tool on your computer or server.

6. Learn and experiment: Familiarize yourself with the simulation tool by studying its documentation, tutorials, and examples. Experiment with different configurations, parameters, and data generation settings to understand its capabilities and generate synthetic data that meets your requirements.

7. Validate and refine: Validate the generated synthetic data against real data or known ground truth to ensure its quality and accuracy. Refine the simulation tool's parameters or algorithms as needed to improve the fidelity of the generated data.

8. Community and support: Join relevant forums, communities, or discussion groups associated with the simulation tool. Engage with other users or developers to seek guidance, share experiences, and resolve any issues you encounter during the data generation process.

9. Customize or develop your own tool: If existing simulation tools do not meet your specific needs, consider customizing an open-source tool or developing your own simulation tool.

One option for a simulation tool to generate synthetic data is Simulink, which is a popular software tool commonly used for modeling and simulating dynamic systems.

Suggest you have a look at the Open Modelica web site

https://openmodelica.org/ which provides many open-source resources for modeling of cyber-physical systems.

"OPENMODELICA is an open-source Modelica-based1 modeling and simulation environment intended for industrial and academic usage. Its long-term development is supported by a non-profit organization – the Open Source Modelica Consortium (OSMC). An overview journal paper is available and slides about Modelica and OpenModelica.

The goal with the OpenModelica effort is to create a comprehensive Open Source Modelica modeling, compilation and simulation environment based on free software distributed in binary and source code form for research, teaching, and industrial usage. We invite researchers and students, or any interested developer to participate in the project and cooperate around OpenModelica, tools, and applications."