Cosmo Tech

Innovative Modeling Tools

Level-up your modeling and data science game using state of the art modeling
and data science libraries, methodologies and visualization tools

In addition to the business intelligence (BI) component that provides visualizations of simulations and multi-dimensional analysis, the Cosmo Tech simulation can also be integrated with advanced data science libraries including machine learning, scientific computing, optimization, and design of experiments. This integration is particularly achieved by utilizing Spark computing libraries. Users have the ability to create simulation-based experiments by developing their own Python analytics. Data scientists can utilize well-known frameworks such as JupyterLab and PySpark libraries to implement algorithms like uncertainty and sensitivity analysis, parameter optimization with constraints, and pre/post-processing analysis. These algorithms seamlessly utilize our simulation engine through Spark.

CoMETS Library

A suite of tools and methodologies to explore the full potential of simulating complex systems providing a variety of advanced experiments for interacting with numerical models and simulators. Each of these experiments offers unique capabilities to accurately mirror real-life scenarios, unlock hidden insights, optimize outcomes and enhance decision making.


Fine-tune the model behavior and swiftly integrate simulation into data scientist workflows. The Cosmo Tech simulators and the CoMETS Advanced Experimentation library are compatible with JupyterLab, a powerful tool that allows users to fine-tune the model behavior and swiftly integrate simulation into data scientist workflows, thus bridging the gap between data science and simulation.