In traditional system identification, we estimate a model of an unknown dynamical system based on given input/output sequences and available physical knowledge. Yet, is it also possible to understand the intricacies of dynamical systems not solely from their input/output patterns, but 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 pretrain a meta model that represents a class of dynamical systems. This meta model is trained from a potentially infinite stream of synthetic data, generated by systems randomly extracted from a certain distribution. At its core, the meta model serves as an implicit representation of the main characteristics of a class of dynamical systems. When provided with a brief context from a new system - specifically, a short input/output sequence - the meta model implicitly discerns its dynamics, enabling predictions of its behavior. The proposed approach harnesses the power of Transformer architectures, renowned for their in-context learning capabilities in Natural Language Processing tasks. 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：在传统系统辨识中，我们基于给定的输入/输出序列及已知物理知识来估计未知动力学系统的模型。然而，是否可能不仅通过输入/输出模式，还能通过观察同一类中其他系统的行为来理解复杂动力学系统？这一核心问题驱动了本文的研究。针对此问题，我们提出了一种新的系统辨识范式，主要解决两个任务：单步预测和多步仿真。与传统方法不同，我们并不直接估计特定系统的模型，而是预训练一个能代表某类动力学系统的元模型。该元模型通过可能无限长的合成数据流进行训练，这些数据由从特定分布中随机抽取的系统生成。本质上，元模型隐式表征了一类动力学系统的主要特征。当提供来自新系统的简短上下文（即短输入/输出序列）时，元模型能隐式辨识其动力学特性，从而对其行为进行预测。所提方法利用了Transformer架构的优势——该架构以其在自然语言处理任务中的上下文学习能力而著称。对于单步预测，我们采用类似GPT的解码器专用架构；而仿真问题则使用编码-解码结构。初步实验结果肯定地回答了我们的基本问题，为系统辨识领域开辟了新的研究方向。