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FoundPDE: New foundation model for symbolic discovery of PDEs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed FoundPDE, a generative foundation model designed for the symbolic discovery of partial differential equations (PDEs) from data. This transformer-based model utilizes Fourier and spatial-derivative augmentation to extract information from trajectories and refine coefficients. FoundPDE demonstrates strong performance on a benchmark of 20 PDE families, achieving 97.50% type recognition, and maintains robustness under coefficient shifts and noise. The model also supports few-shot fine-tuning for discovering unseen composite equations and can be extended to new physics with limited additional data. AI

IMPACT Enables more efficient and generalizable discovery of scientific laws directly from data.

RANK_REASON The cluster contains a research paper detailing a new model for symbolic discovery. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

FoundPDE: New foundation model for symbolic discovery of PDEs

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The cluster contains a research paper detailing a new model for symbolic discovery. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Langchen Liu, Junlin Luo, Min Zhu, Zhuoran Yang, Lu Lu ·

    FoundPDE: A generative foundation model for data-driven symbolic discovery of partial differential equations

    arXiv:2610.26963v1 Announce Type: cross Abstract: Discovering governing partial differential equations (PDEs) from observed data is a central inverse problem in computational science and engineering: the goal is not only to predict trajectories, but to identify interpretable symb…

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