Designing complex behaviour: novel pathways for assisting design based on dynamics-informed machine learning in structural mechanics
This project aims at developing new machine learning approaches to derive design assistants for the nonlinear dynamical behaviour of structures under complex transient loads from small data. To provide the engineer with a formalized objective for designing a structure’s dynamical behaviour under hundreds of time-resolved scenarios, novel Dynamics-Informed Reservoir Computers (DIRCs) are proposed. This new systematic design strategy strives at adapting the prediction model structure and complexity to the individual system dynamics, thereby arriving at highly accurate surrogate models with small data requirements and real-time capabilities. Their modularity makes DIRCs are highly flexible and thus easy to integrate into existing toolsets, while having a minimal ecological footprint compared to data-hungry and compute-intense deep learning techniques that are widely applied today.