LLM-ODE integrates large language models into genetic programming to guide symbolic search for governing equations of dynamical systems, outperforming classical GP on 91 test cases in efficiency and solution quality.
In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery
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SAGE-Fit improves symbolic regression by exploiting structure and semantic priors to optimize parameters in non-convex inner loops, reducing under-scoring of correct equation structures.
DoLQ employs a sampler agent, parameter optimizer, and LLM-based scientist agent to iteratively propose, refine, and evaluate ODE candidates, yielding higher success rates and better symbolic term recovery than prior symbolic regression methods on multi-dimensional benchmarks.
citing papers explorer
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LLM-ODE: Data-driven Discovery of Dynamical Systems with Large Language Models
LLM-ODE integrates large language models into genetic programming to guide symbolic search for governing equations of dynamical systems, outperforming classical GP on 91 test cases in efficiency and solution quality.
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When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization
SAGE-Fit improves symbolic regression by exploiting structure and semantic priors to optimize parameters in non-convex inner loops, reducing under-scoring of correct equation structures.
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Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation
DoLQ employs a sampler agent, parameter optimizer, and LLM-based scientist agent to iteratively propose, refine, and evaluate ODE candidates, yielding higher success rates and better symbolic term recovery than prior symbolic regression methods on multi-dimensional benchmarks.