On the adaptation of in-context learners for system identification
This work addresses the need for more robust and versatile meta-learning frameworks in system identification, though it appears incremental in nature.
The paper tackled the problem of adapting meta-models in in-context system identification to improve predictive performance, demonstrating effectiveness in tailoring, extending, and recalibrating models across three realistic scenarios.
In-context system identification aims at constructing meta-models to describe classes of systems, differently from traditional approaches that model single systems. This paradigm facilitates the leveraging of knowledge acquired from observing the behaviour of different, yet related dynamics. This paper discusses the role of meta-model adaptation. Through numerical examples, we demonstrate how meta-model adaptation can enhance predictive performance in three realistic scenarios: tailoring the meta-model to describe a specific system rather than a class; extending the meta-model to capture the behaviour of systems beyond the initial training class; and recalibrating the model for new prediction tasks. Results highlight the effectiveness of meta-model adaptation to achieve a more robust and versatile meta-learning framework for system identification.