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

Bootstrapping OTS-Funcimg Pre-training Model (Botfip) -- A Comprehensive Symbolic Regression Framework