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 represents a class of dynamical systems. This meta model is trained on a potentially infinite stream of synthetic data, generated by simulators whose settings are randomly extracted from a probability distribution. When provided with a context from a new system-specifically, an input/output sequence-the meta model implicitly discerns its dynamics, enabling predictions of its behavior. The proposed approach harnesses the power of Transformers, renowned for their \emph{in-context learning} capabilities. For one-step prediction, a GPT-like decoder-only architecture is utilized, whereas the simulation problem employs an encoder-decoder structure. Initial experimental results affirmatively answer our foundational question, opening doors to fresh research avenues in system identification.

翻译：能否不仅通过输入/输出模式，还通过观察同一类别中其他系统的行为来理解动态系统的复杂性？这一核心问题驱动了本文的研究。针对这一问题，我们提出了一种新颖的系统辨识范式，涵盖两个主要任务：单步超前预测与多步仿真。与传统方法不同，我们并非直接估计特定系统的模型，而是学习一个代表动态系统类别的元模型。该元模型基于由随机从概率分布中提取设置的仿真器生成的潜在无限合成数据流进行训练。当提供来自新系统的上下文（具体为输入/输出序列）时，元模型可隐式识别其动力学特性，从而实现对其行为的预测。所提方法利用了以"上下文学习"能力著称的Transformer架构。对于单步预测，采用类似GPT的解码器仅结构；而仿真问题则采用编码器-解码器结构。初步实验结果对基础问题给出了肯定回答，为系统辨识领域开辟了新的研究方向。