  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.
