Standardize your simulation as a function.

Funz is a parametric & parallel remote execution engine to wrap scientific computing as a function.
Then, just use it as an objective function for any standard algorithm (eg. optimization).
Funz is available for Windows, Mac OS, Linux, through command-line (Bash, Cmd.exe), Python, R.


Parametric scientific computing ?

  1. Take a "parametrized" input file (contains $x, $y,... replacing some numerical values),
  2. Define values taken by these parameter (or let a dedicated algorithm do),
  3. Run calculations on remote ressources,
  4. Parse results & analyse.
Funz eases this "functional" wrapping of your simulation.
Basic demo in Python: Open In Colab , or R: Open In Colab



  • A standardized execution script (properly exiting when calcualtion failed),
  • A set of reserved characters (like '$') for input files parametrization,
  • Some regular expression to extract output values of interest.


  1. Parse & compile input files with given parametric syntax,
  2. Launch remote execution of each input combination given (async on dedicated TCP channel),
  3. Call external algorithm to drive calculation parameters values,
  4. Monitor & restart failed calculations (blacklisting unsuitable computers),
  5. Store & parse output results.


  • Wrapper for any simulation software (holds I/O files, ASCII & common formats: HDF5 NetCDF XML JSON),
  • Algorithm for design of experiments (supports definition of input values regarding engineering objective).

Examples ?

Funz makes your simulations callable as functions for any of cmd.exe / bash / python / R / Java:
  • From Excel spreadsheet "sheet.xlsx", get cell commented with "=total" for each 10 different values of cell commented with "$income":
    Funz.bat Run -m Excel -if sheet.xlsx -iv income=10,20,30,35,40,42,45,47,50,52 -oe total
  • Search convection coefficient which leads to a minimal temperature of 25.5 degrees with OpenModelica PDE solver: RunDesign -m OpenModelica -if -iv convection=[0.5,1.0] -oe "min(T)" -d Brent -do ytarget=25.5
  • Build a response surface of MySimulator for x1 in [-5,5] and x2 in [-5,5]:
    # pip install Funz
    import Funz
    X = {'x1':np.arange(-5, 5, 0.25),'x2':np.arange(-5, 5, 0.25)}
    y = Funz.Run(model="MySimulator",input_files=["inputfile1","inputfile2"],input_variables= X ,all_combinations=True)['y']
    # mplt.plot_surface(X['x1'], X['x2'], y)
  • Propagate x1 as uniform random and x2 as normal random uncertainties through MySimulator:
    # devtools::install_github("Funz/Funz.R")
    hist( Funz::Run(model="MySimulator",input.files=c("inputfile1","inputfile2"),input.variables= list(x1=runif(100),x2=rnorm(100)) )['y'] )
  • Chain simulations:
    # pip install Funz
    import Funz
    X = {'x1':..., 'x2':...}
    # first simulator, inputfile1 and inputfile2 contain 'x1' and 'x2' variables
    y1 = Funz.Run(model="MySimulator1",input_files=["inputfile1","inputfile2"],input_variables= X)['y']
    # second simulator, depends on results of first one also, inputfile3 contains 'x1', 'x2' and 'y1' variables
    y2 = Funz.Run(model="MySimulator2",input_files=["inputfile3"],input_variables= X.update(y1))['z']
    # post-processing in Excel (postrpro.xlsx sheet contains cells commented by '$x1' or '$x2'), returns cell value commented as "=result"
    result  = Funz.Run(model="Excel",input_files=["postpro.xlsx"],input_variables= X.update(y2))['result']
  • Identify the most influent input parameters
  • Max/minimize simulation output value
  • Build inverse model

Who we are ?

Early versions of Funz project were funded by IRSN (French institute for radiological protection and nuclear safety).
IRSN open-sourced Funz in 2019 to support safety and design engineering community.