Making models deal with the complexity of modern engineering systems

November 22, 2022

PhD researcher Fahim Shakib developed new data-driven tools to construct compact prediction models.

Fahim Shakib
Fahim Shakib

Mathematical models play an indispensable role in science, technology, and society in the modern world. PhD researcher Fahim Shakib has developed tools that use data to ensure the models are compact, fast and can deal with complex engineering systems. Shakib’s succesfully defended his thesis on Wednesday 23 November at the department of Mechanical Engineering and received a cum laude for his work.

In engineering systems, models are used for the model-based design to predict the system's future performance, without the need for prototyping, controller deployment, or time-consuming experimentation.

Subsequently, designs can be improved, leading to enhanced performance with fast design cycles. Such engineering systems have been key enablers of uncountable many technological breakthroughs.

Complex and non-linear

There is a drawback though. As engineering systems are becoming increasingly complex and modeling accuracy requirements increasingly stringent, the derived non-linear models are often too complex for model-based design.

For example, testing such a complex model can be computationally demanding and sometimes even infeasible due to limited computational and data storage capabilities as well as time restrictions. To reduce the computational expense, the complex model is often replaced by a reduced model that is less complex but still sufficiently accurate.

Driven by data

Another alternative is data-driven modeling. Such an approach uses observed input and output data from the system to directly construct compact models that closely describe this data.

Therefore, data-driven modeling is typically fast and enables system modeling if first-principle modeling is infeasible. For example, data-driven modeling provides basic understanding of our brain function, whereas first-principle modeling of our brain function is considered a challenging task.

New modeling tools

In his research, Fahim Shakib has developed a number of tools to reduce the complexity of the models while preserving their key properties, with minimal accuracy loss.

He also presents fast data-driven modeling tools that use experimental data obtained from the system and deliver guaranteed model stability.

In both cases, certified model stability is a key enabler in safely using these models in unseen and generalized scenarios for model-based design.

More information

Fahim Shakib, Data-driven modeling and complexity reduction for nonlinear systems with stability guarantees, Supervisors: Nathan van der Wouw, AV Pavlov, Sasha Progromskiy.

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Henk van Appeven
(Communications Adviser)

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