This　paper　proposes　a　novel　technique　for　automated　neural　network　based　multiphysics　parametric　modeling　of　microwave　components.　For　the　first　time,　we　propose　to　utilize　automated　model　generation　(AMG)　algorithm　in　the　field　of　electromagnetic　(EM)　centric　multiphysics　parametric　model　development　to　improve　the　neural-based　multiphysics　modeling　efficiency.　All　the　subtasks　in　developing　a　neural　network　based　multiphysics　parametric　model,　including　EM　centric　multiphysics　data　generation,　neural　network　structure　adaptation,　training　and　testing,　are　integrated　into　one　unified　and　automated　framework,　thus　converting　the　conventional　human-based　manual　modeling　into　an　automated　computational　process.　In　the　proposed　algorithm,　automated　EM　centric　multiphysics　data　generation　is　realized　by　automatic　driving　of　multiphysics　simulation　tools.　Parallel　computation　technique　is　incorporated　to　further　speedup　the　data　generation　process　by　driving　multiple　EM　centric　multiphysics　simulations　on　parallel　computers　simultaneously.　In　addition,　automated　neural　model　structure　adaptation　algorithm　for　multiphysics　parametric　modeling　is　also　proposed.　In　this　way,　the　proposed　technique　automates　the　neural-based　multiphysics　model　development　process　and　significantly　reduces　the　intensive　human　effort　and　modeling　time　demanded　by　the　conventional　manual　multiphysics　modeling　methods.　The　achieved　neural　model　can　be　used　to　provide　accurate　and　fast　prediction　of　the　EM　centric　multiphysics　responses　of　microwave　components　in　high-level　multiphysics　design.　Examples　of　multiphysics　parametric　modeling　of　two　microwave　filters　are　presented　to　show　the　advantage　of　this　work.