@@ -47,12 +47,24 @@ Instructions for Debian/Ubuntu based workstations are as follows.
*[generateRF](./generateRF) contains all the scripts used to generate and vizualize the random fields
*[cellRF](./cellRF) contains an example in which we generate a single variable random field on a lattice unit cell
*[rnnRF](./rnnRF) contains an example in which we generate a correlated two-variable random field and the siulations script to run Monte Carlo simulations on a multiscale lattice compression using a [MOAMMM stochastic neural network](https://gitlab.uliege.be/moammm/moammmPublic/syntheticdata/sveresponses) as surrogate of the cell response.
*[rnnRF](./rnnRF) contains an example in which we generate a correlated two-dimension random field and the siulations script to run Monte Carlo simulations on a multiscale lattice compression using a [MOAMMM stochastic neural network](https://gitlab.uliege.be/moammm/moammmPublic/syntheticdata/sveresponses) as surrogate of the cell response.
### generateRF
*[generateRF.py](./generateRF/generateRF.py):
* The RF is defined from the mean and standard deviation of the variables as well as from their correlation following for example for a 2-dimension RF:
meanX = np.array([0.5,10]) # mean of variables
stdX = np.array([0.05,0.1]) # std of variables
rho = 0.2
R0_XY = np.array([[1, rho], [rho, 1]])
* And from a the correlation lengths along the 3 direction
lc = [15., 15., 15.] # correlation length
* The number of RFs to be generated follows from
Nsim =10
* The Random fields will be generated at the spatial points defined in terms of their coordinates.
One example of points coordinates can be found in [rnnRF/GPData/oordinate_P_ZZ_OnPhysical_11.csv](./rnnRF/GPData/oordinate_P_ZZ_OnPhysical_11.csv) stored following 3 coordinates, point number and point volume (for a FE simulation)
The script tries to read a file in 'mechDir+''/GPData'' '
