Has Interprocess Communication Been Used for Multi-Phase Macroscopic Analyses?

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SUMMARY

The discussion centers on the application of interprocess communication (IPC) for conducting multi-phase macroscopic analyses using various computational codes. Specifically, it highlights the potential for finite element codes, dynamics codes, and AI codes to communicate via sockets while adhering to a standardized data format. The motivation is to explore how isolating these massive codes could facilitate learning and integration with AI tools, despite potential speed trade-offs. References to the July 2018 issue of Benchmark magazine by NAFEMS are suggested for further insights into AI applications in simulation processes.

PREREQUISITES
  • Understanding of interprocess communication (IPC) techniques
  • Familiarity with finite element analysis (FEA) software
  • Knowledge of dynamics simulation codes
  • Basic concepts of artificial intelligence in computational contexts
NEXT STEPS
  • Research socket programming for interprocess communication
  • Explore multi-phase mechanics codes and their integration
  • Investigate the use of AI in simulation processes
  • Review the July 2018 issue of Benchmark magazine for case studies
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This discussion is beneficial for computational engineers, software developers in simulation technologies, and researchers interested in integrating AI with multi-phase analysis tools.

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Integrating the processes of mechanics using network softare
I am aware that a lot of software exists to conduct such multi-phase macroscopic analyses.

But has anyone ever used the tools of interprocess communication to do this?

Consider, for example, these three:
  • A Finite element code (For deformation) on a parallel machine
  • A Dynamcis code (for dynamics) on a vector machine
  • An AI code (for learning) on a PC
Consider a case where all such codes communicate through, say, sockets.

Consider that this is done in such a way that each code adheres to a prescribed data format so that they can more readily communicate?

Can anyone share any references?

The motivation for this question is as follows...

There exist many multi-phase mechanics codes (dynamics, FE, Fluids, Heat, etc.), however, the codes are massive. Perhaps, if each code was more isolated, then it would be easier:

  • to learn how to do multi-phase analyses (again, at the macroscopic level).
  • to share efforts.
  • to integrate the codes with, say, learning tools for smart machines.

I am just thinking out of the box and would like to know if anyone has done this (I know it will not be as fast as one stand alone code).
 
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I think that you will find this issue of Benchmark magazine (by NAFEMS) interesting: „Artificial Intelligence & Machine Learning” July 2018. It features several articles with examples of industrial cases when AI was applied to simulation processes.
 

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