LLM-Based Agentic AI Creates Interpretable Surrogate for Gravitational Waveforms
A team of researchers has introduced GWAgent, a workflow powered by a large language model (LLM) that generates interpretable analytic surrogates directly from simulation data. This agentic AI system creates surrogate models for costly simulations, which are often considered black boxes. As a case study, GWAgent produced a surrogate for gravitational waveforms resulting from eccentric binary black hole mergers. By incorporating a physics-informed domain ansatz, the accuracy of the output model saw significant enhancement. The resulting analytic surrogate achieved a median Advanced LIGO mismatch of 6.9×10⁻⁴ and delivered an approximate 8.4× acceleration in waveform evaluation, outperforming both symbolic regression and traditional machine learning methods. This research was published on arXiv under ID 2605.11280.
Key facts
- GWAgent is an LLM-based workflow for constructing interpretable analytic surrogates.
- The surrogate models are built directly from simulation data.
- Demonstrated on gravitational waveforms from eccentric binary black hole mergers.
- Physics-informed domain ansatz improved model accuracy.
- Median Advanced LIGO mismatch of 6.9×10⁻⁴.
- Approximately 8.4× speedup in waveform evaluation.
- Outperforms symbolic regression and conventional ML baselines.
- Published on arXiv as 2605.11280.
Entities
Institutions
- arXiv
- Advanced LIGO