Optimising Complex Systems

Complex systems, by their very nature, are cross-functional, high-dimensional, have inherently complex interdependences between the individual components and generally exhibits, amongst other characteristics, non-linearity and dependency to changes of external conditions. Optimising complex systems therefore generally requires a well orchestrated multi-disciplinary modelling effort, besides setting clear goals and defining the right optimisation strategy, accuracy and trade-offs. Complex systems are found not only in engineering, but also across various fields of science and social sciences.

The quality of the optimisation strongly relies on the quality of the modelling, and therefore designing the correct model architecture and defining the right modelling strategy should be treated as a cross-functional project and carefully planned from the optimisation goals. Once the optimisation requirements are defined, modelling involves decomposing the system into subsystems, simplifying where possible, and then reintegrating them into a cohesive whole. While engineering often relies on physical models, behavioural and empirical models-common in other fields- are useful at subsystem level, especially when knowledge is limited, uncertainty is high, or fast iteration is needed. Model should also be designed to enable quick correlation with experimental data to assess predictive accuracy and remain easily updatable to ensure adaptability and rapid response to changing requirements.

The famous quote often attributed to Albert Einstein ‘Everything should be made as simple as possible but not simpler’ is particularly true for models for optimisation. Striving for simplicity in modelling is not only a matter of efficiency, it is also importantly connected to smoother cost functions, crucial for an effective and reliable optimisation.

Optimising a complex system often requires balancing trade offs between competing requirements and different strategies can be employed to manage them and guide the decision-making process. Moreover the outcomes of the optimisation should demonstrate robustness to changes in external conditions, acknowledging the inherent uncertainty present in real-world application.

Finally, optimisation in complex, cross-functional environment also requires harmonising the contributions of experts across disciplines. Success depends not only on the technical excellence but also on fostering trust, promoting collaboration, and aligning diverse perspectives towards a common goal. In this sense, optimising the systems also become a matter of orchestrating human expertise, today also increasingly augmented by the fast-evolving capabilities of a connected world and artificial intelligence.

With over two decades of experience in high-performance engineering, Lucia Conconi has led cutting-edge projects in Formula 1, working with top teams such as Mercedes GP, Renault Sport Racing, and Alfa Romeo F1 Team.

As a former Head of Vehicle Performance, Lucia Conconi specialized in vehicle dynamics, simulation, data-driven development, and team leadership in fast-paced, innovation-driven environments.

• Holding a degree in Aerospace Engineering from Politecnico di Milano, Lucia Conconi applies experience of F1 and motorsport methodologies to drive performance and efficiency across various fields and industries.

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