FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model

• URL: http://arxiv.org/abs/2404.14688v2
• Date: Wed, 22 May 2024 16:43:48 GMT
• Title: FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model
• Authors: Zezheng Song, Jiaxin Yuan, Haizhao Yang,
• Abstract summary: We propose a pre-trained foundation model textbfFMint (textbfFoundation textbfModel based on textbfInitextbftialization) It is designed to speed up large-scale simulations of various differential equations with high accuracy via error correction.
• Score: 5.748690310135373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a pre-trained foundation model \textbf{FMint} (\textbf{F}oundation \textbf{M}odel based on \textbf{In}i\textbf{t}ialization), designed to speed up large-scale simulations of various differential equations with high accuracy via error correction. Human-designed simulation algorithms excel at capturing the fundamental physics of engineering problems, but often need to balance the trade-off between accuracy and efficiency. While deep learning methods offer innovative solutions across numerous scientific fields, they frequently fall short in domain-specific knowledge. FMint bridges these gaps through conditioning on the initial coarse solutions obtained from conventional human-designed algorithms, and trained to obtain refined solutions for various differential equations. Based on the backbone of large language models, we adapt the in-context learning scheme to learn a universal error correction method for dynamical systems from given prompted sequences of coarse solutions. The model is pre-trained on a corpus of 600K ordinary differential equations (ODEs), and we conduct extensive experiments on both in-distribution and out-of-distribution tasks. FMint outperforms various baselines on large-scale simulation, and demonstrates its capability in generalization to unseen ODEs. Our approach achieves an accuracy improvement of 1 to 2 orders of magnitude over state-of-the-art dynamical system simulators, and delivers a 5X speedup compared to traditional numerical algorithms.

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