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SymbolicGPT: A Generative Transformer Model for Symbolic Regression

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arxiv 2106.14131 v1 pith:6JFS4VXW submitted 2021-06-27 cs.LG cs.CLcs.SC

Mojtaba Valipour , Bowen You , Maysum Panju , Ali Ghodsi This is my paper
classification cs.LGcs.CLcs.SC
keywords modelregressionsymboliclanguagemathematicalmodelssymbolicgptaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal
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Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values. Due to the richness of the space of mathematical expressions, symbolic regression is generally a challenging problem. While conventional approaches based on genetic evolution algorithms have been used for decades, deep learning-based methods are relatively new and an active research area. In this work, we present SymbolicGPT, a novel transformer-based language model for symbolic regression. This model exploits the advantages of probabilistic language models like GPT, including strength in performance and flexibility. Through comprehensive experiments, we show that our model performs strongly compared to competing models with respect to the accuracy, running time, and data efficiency.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization

    cs.LG 2026-02 conditional novelty 7.0 of 10

    PiT-PO adaptively fine-tunes an LLM during symbolic regression search using physics-validity and token-level redundancy constraints, reporting state-of-the-art benchmark results and a periodic-hill turbulence closure.

  2. Verifier-Guided Model Discovery for Physical Dynamical Systems with Pretrained Symbolic Transformers

    cs.LG 2026-08 conditional novelty 6.0 of 10

    Verifier-guided selection lets a pretrained symbolic transformer transfer from synthetic ODEs to high-dimensional cylinder-flow data, recovering symbolic vortex-shedding models that generalize across Reynolds numbers.

  3. MOT-SR: Multi-Objective Tool-Augmented Scientific Equation Discovery with Large Language Models

    cs.LG 2026-07 conditional novelty 6.0 of 10

    MOT-SR combines tool-augmented data analysis with multi-objective Pareto selection to discover symbolic equations, outperforming LLM-based and classical SR baselines on benchmarks and an EMRI orbital-correction task.

  4. Symbolic Regression for Shared Expressions: Introducing Partial Parameter Sharing

    cs.LG 2026-01 conditional novelty 6.0 of 10

    Introduces partially-shared parameters for symbolic regression with multiple categorical variables, matching prior fit quality on a supernovae dataset with fewer parameters.

  5. DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience

    cs.LG 2025-06 conditional novelty 5.0 of 10

    DrSR improves LLM-based symbolic regression by adding data-aware structural insights and a reflective idea library, beating prior methods on six benchmark tasks.

  6. SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery

    cs.CV 2025-06 reject novelty 4.0 of 10

    SatelliteFormula couples a Swin Transformer image encoder with a symbolic regression decoder to generate expressions for indices such as NDVI and biomass from satellite imagery.

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