Machine Learning, AI, Data, Complex Systems, Simulation

December 2024

Dione Complex Systems studies machine learning / AI and (big) data analysis methods in the context of complex systems to discover useful knowledge from large amounts of data.

Computer simulation based on the discovered knowledge enables analysis of what-if scenario's and impacts of risk and uncertainty.

Machine Learning / AI

Examples of methods:

  • Neural networks
  • Evolutionary algorithms
  • Bayesian networks
  • Decision trees
  • Regression

Data Sources

Data sources:

  • Databases
  • Data streams
  • Monitoring
  • Measuring intruments
  • Website traffic, microservices

Modeling and Simulation

  • We model real-world systems and implement these models in computer simulations
  • With simulations we can run experiments and predict the behavior of a system under various conditions
  • This makes it possible to make informed decisions in a cost-effective and time-efficient manner

Data Use

  • We use data to create accurate models
  • We use input data to run scenarios
  • We compare simulation results with real-world data
  • We integrate real-time data into simulations to continuously update and refine our models, providing more accurate and timely insights