Discovery of Interpretable Surrogates via Agentic AI: Application to Gravitational Waves

ArXi:2605.11280v1 Announce Type: cross Fast surrogate models for expensive simulations are now essential across the sciences, yet they typically operate as black boxes. We present \texttt{GWAgent}, a large language model (LLM)-based workflow that constructs interpretable analytic surrogates directly from simulation data. Surrogate modeling is well suited to agentic workflows because candidate models can be quantitatively validated against ground-truth simulations at each iteration. As a nstration, we build a surrogate for gravitational waveforms from eccentric binary black hole mergers.