Bootstrapping OTS-Funcimg Pre-training Model (Botfip) -- A Comprehensive Symbolic Regression Framework
This addresses the problem of insufficient multimodal frameworks in scientific computing for researchers, but it appears incremental as it adapts ideas from image-text models to a new domain.
The authors tackled the lack of deep multimodal information mining in scientific computing by proposing Botfip, a framework based on Function Images and Operation Tree Sequence, and validated its advantages in low-complexity symbolic regression problems, showing potential for broader applications.
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.