Videos

Introducing ESI (TNO)

is a leader when it comes to high-tech manufacturing. This concerns complex systems such as industrial printers, machines that make sophisticated computer chips, medical systems for hospitals. To preserve that lead, and to ensure that Dutch competitiveness is given a boost, ESI as an open innovation knowledge center performs research and development into methods to improve and further develop such complex highly digitalised systems. ESI. Managing complexity.

Software behaviour: change impact analysis and model learning

Have you ever struggled to understand the behaviour of a piece of software? Have you ever seen a change in software cause a regression in a seemingly unrelated part of the system?
These are the kinds of challenges we address. Complex high-tech systems typically consist of many concurrently communicating software components. Understanding the current software behavior is a major challenge.

Early system validation

We consider three important aspects to validate:

  1. functional requirements

  2. architectural decomposition

  3. performance indicators

To validate systems before realizing them, you need lightweight simulation that abstracts from as many details as possible.

Reference architecture

The principles of how to reason from customer value to technical choices and vice versa.
This is generally very challenging as there are many steps between technical realization and emerging system properties, which are what customers actually experience.

DSL on your menu

How can DSL help you and your company to manage system complexity?

Prisma

Prisma, an ESI project. A domain model-centric approach for the development of large-scale office lighting systems.

MBSA

MBSA - an ESI (TNO) project. Model Based System Architecting. Systematic quantitative early architecting.

ESI digital twin

From virtual prototype to digital twin. In traditional model-driven development, models are used to guide system development. Models are used to analyze aspects of the system under development and identify the appropriate trade-off between different system qualities. To analyze a system’s behavior, simulation models are used; these virtual prototypes allow analysis and optimization of system behavior under realistic usage scenarios. The simulation models do not lose their value when the system has become operational. Then they can be used as digital twins to analyze the system’s performance. The simulation models can be used as a reference to identify anomalous or suboptimal system behavior.