  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.
