In the field of scientific computing, many problem-solving approaches tend to focus only on the process and final outcome, even in AI for science, there is a lack of deep multimodal information mining behind the data, missing a multimodal framework akin to that in the image-text domain. In this paper, we take Symbolic Regression(SR) as our focal point and, drawing inspiration from the BLIP model in the image-text domain, propose a scientific computing multimodal framework based on Function Images (Funcimg) and Operation Tree Sequence (OTS), named Bootstrapping OTS-Funcimg Pre-training Model (Botfip). In SR experiments, we validate the advantages of Botfip in low-complexity SR problems, showcasing its potential. As a MED framework, Botfip holds promise for future applications in a broader range of scientific computing problems.

翻译：在科学计算领域，许多问题求解方法往往只关注过程与最终结果，即便是AI for science领域，也缺乏对数据背后深层多模态信息的挖掘，缺少类似图像-文本领域的多模态框架。本文以符号回归（Symbolic Regression, SR）为切入点，借鉴图像-文本领域的BLIP模型，提出一种基于函数图像（Funcimg）与操作树序列（OTS）的科学计算多模态框架，命名为自举OTS-函数图像预训练模型（Botfip）。在符号回归实验中，我们验证了Botfip在低复杂度符号回归问题中的优势，展现了其潜力。作为一种多模态编码-解码（MED）框架，Botfip有望在未来应用于更广泛的科学计算问题。