The research group on modelling and simulation has developed over the years a number of codes to address complex problems in nonlinear solid, structural, and fluid mechanics.

IRIS

This is the main code of the group. It was started in 2001 and it now can address a large number of problems in the context of computational mechanics. See its dedicated web page.

In addition to IRIS, the group has developed other codes that might be useful for researchers in computational fluid, solid, and structural mechanics. The following are open-source codes that are freely available; a description is followed, for each of them, with a link to a public repository where the code can be downloaded from:

MUESLI

This is the Material UnivErSal LIbrary, a library of C++ classes that implement consitutive laws for solids and fluids, and also some section constitutive laws for structures. This library is currently stored as an open project in the repository bitbucket, although the code can also be downloaded from https://gitlab.com/ignacio.romero/muesli. See also the dedicated web page . This is the Material UnivErSal LIbrary, a library of C++ classes that implement consitutive laws for solids and fluids, and also some section constitutive laws for structures. This library is currently stored as an open project in the repository bitbucket, although the code can also be downloaded from https:// . See also the *dedicated web page *.

PYRIS

This is a very compact finite element code written in Python. The code is very small and easy to undertand, so it can be used in courses that teach the finite element code. It can be obtained from: https://bitbucket.org/ignromero/pyris/get/1a54e295943b.zip

R2BT

R2BT is a C++ library with several algorithms for solving the neighbourhood problem in large set of points. It can be downloaded form https://bitbucket.org/stapia/r2bt/get/679d77e10050.zip.

ACBICI

This is an open-source python library for the calibration of models based on Bayesian techniques. It can calibrate inexpensive and expensive models, the latter by using surrogate meta-models. It can also be used to estimate the so-called model discrepancy and the error in experimental measurements. See its dedicated web page.