arXiv:2308.13380v3 Announce Type: replace-cross Abstract: Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same class? This central question drives the study presented in this paper. In response to this query, we introduce a novel paradigm for system identification, addressing two primary tasks: one-step-ahead prediction and multi-step simulation. Unlike conventional methods, we do not directly estimate a model for the specific system. Instead, we learn a meta model that r
The paper signals a current direction in AI research towards more robust and generalizable system understanding, moving beyond individual system training to class-based models.
This new paradigm could dramatically improve the efficiency and applicability of AI in complex dynamic systems, leading to faster development and broader deployment of autonomous technologies.
The focus shifts from creating isolated models for specific systems to developing meta-models capable of understanding entire classes of systems through in-context learning, reducing the need for extensive individual system data.
- · AI researchers
- · Automation industries
- · Robotics
- · Complex system operators
- · Traditional system identification methods
- · Data-intensive modeling approaches
More efficient and generalizable AI models for dynamic systems.
Accelerated development and deployment of autonomous systems operating in varied environments.
Reduced barriers to entry for new AI applications in complex physical and digital domains due to less specific training data requirements.
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