ASPIRE: An Agentic System-Identification Platform for Iterative Reasoning and Exploration

arXiv:2611.13114v1 Announce Type: cross Abstract: System identification requires expert decisions on the identification method, incorporation of prior physical knowledge, and model validation. We propose ASPIRE, a framework for nonlinear system identification in which a large language model agent selects and executes the identification method. ASPIRE constructs closed-form, nonlinear static or dynamic models from measured data, optionally supplemented by a system description, the intended use of the model, and physical requirements expressed formally or in natural language. Each model includes

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