Scientific Machine Learning (SciML) has advanced recently across many different areas in computational science and engineering. The objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing and analysis are still time consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate MyCrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of MyCrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a webapp based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend MyCrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus in fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology and bioinformatics.

翻译：科学机器学习（SciML）近期在计算科学与工程的多个领域取得了进展。其目标是在无需采用复杂且计算代价高昂的数据同化方案的情况下，无缝整合数据与物理规律。然而，预处理、问题建模、代码生成、后处理及分析仍然耗时，这可能阻碍SciML在工业应用及数字孪生框架中的广泛适用性。本文中，我们将SciML的各个阶段整合于ChatGPT框架之下，构建了MyCrunchGPT——它扮演着指挥者的角色，基于用户简单的提示词来编排SciML的完整工作流。具体而言，我们通过两个示例展示了MyCrunchGPT在空气动力学翼型优化以及交互式获取不同几何构型流场方面的潜力，其中特别强调了验证阶段。为展示MyCrunchGPT的工作流程并构建可支撑更广泛愿景的基础设施，我们开发了基于Web应用的图形化用户界面，其中包含生成综合性总结报告的选项。总体目标是扩展MyCrunchGPT以处理计算力学、设计、优化与控制中的多样化问题，以及SciML中涉及的一般科学计算任务，从而将其用作研究辅助工具与教育工具。尽管本文的示例聚焦于流体力学，未来版本将瞄准固体力学与材料科学、地球物理学、系统生物学及生物信息学等领域。