In this post, we explore how to integrate a neural network in a user material subroutine (UMAT) for CalculiX. The main idea is that the material’s plastic behavior is learned by a neural network and embedded into the UMAT code, in combination with a classic linear elastic model. We’ll use a UMAT example with the […]
Developing User Material Subroutines in CALCULIX: 4 UMAT neural network example Leer más »
Converting user material subroutines from ABAQUS to CALCULIX can significantly boost flexibility in open-source finite element simulations. Thanks to the ABAQUS UMAT examples shared on GitHub and similar platforms, it is often easier to start from an existing model than to build one from scratch. In this post, we explore how to adapt one of
This blog explains how to compile CALCULIX. This is a critical point required for introducing new user subroutines. The blog is not a comprehensive guide; it is only a description step-by-step of the procedure that we have followed, as an example of the process. Move to /usr/local Edit Make.inc Change: Make the library Unpack Modify
Developing User Material Subroutines in CALCULIX: 2 Compiling CALCULIX Leer más »
This blog initiates a series of post about creating user material subroutines, with illustrative examples. Finite element software, as ABAQUS or CALCULIX supports advanced material modeling through user-defined subroutines, specifically the User Material (UMAT). These subroutines allow for defining complex material behaviors not included in the material models library. In this article, we provide an overview of
This blog is the second part of our series, where we share the Python code used in our paper titled: Bayesian Analysis of Fracture of Polyamide 12 U-Notched Specimens, developed in collaboration with the research group of Professor Jesús Rodríguez-Pérez (DIMME, Grupo de Durabilidad e Integridad Mecánica de Materiales Estructurales, Escuela Superior de Ciencias Experimentales
Recently, we have presented a paper titled: Bayesian analysis of fracture of polyamide 12 U-notched specimens, in collaboration with the group of Professor Jesús Rodríguez-Pérez (DIMME, Grupo de Durabilidad e Integridad Mecánica de Materiales Estructurales, Escuela Superior de Ciencias Experimentales y Tecnología, Universidad Rey Juan Carlos) in the European Conference of Fracture 2024 We share
Ever wondered how engineers and researchers can test the strength and durability of concrete before it’s even mixed? Enter VCCTL (Virtual Cement and Concrete Testing Laboratory), a powerful tool that allows you to model and analyze the mechanical properties of concrete before it’s even mixed. Let’s dive into how you can leverage VCCTL to develop
Cement and Concrete Modeling with VCCTL: A Step-by-Step Guide Leer más »
When fitting data distributed across multiple groups, Bayesian modeling offers a powerful approach to account for uncertainty in parameter estimation. In this blog, we’ll walk through how to fit data from three different groups where each group can be described by a linear regression model, and the three regression lines are parallel).
Bayesian Fitting of Data from Three Groups with Parallel Linear Regression Lines Leer más »
Bayesian statistical fitting is a powerful technique for data analysis that allows us to incorporate prior information and update it with observed evidence. In this blog, we’ll explain how to perform a Bayesian fit for linear regression using Python, with the goal of estimating the model parameters while quantifying uncertainty. This tutorial describes step by
Programming a Bayesian Statistical Fit in Python for Linear Regression Leer más »