6 papers
A data-driven solving strategy based on a greedy optimization algorithm for the analysis of nonlinear beam structures
Thi-Hoa Nguyen, Bruno A. Roccia, Cristian G. Gebhardt
In the last decade, data-driven computational mechanics (DDCM) has emerged as a novel paradigm in computational mechanics, enabling the direct use of constitutive data - such as st…
Solving strategies for data-driven one-dimensional elasticity exhibiting nonlinear strains
Thi-Hoa Nguyen, Viljar H. Gjerde, Bruno A. Roccia +1
In this work, we extend and generalize our solving strategy, first introduced in [1], based on a greedy optimization algorithm and the alternating direction method (ADM) for nonlin…
Data-driven stress problem under purely normal homogeneous Neumann boundary conditions
Cristian G. Gebhardt, Kundan Kumar, Florin A. Radu
Data-Driven Continuum Mechanics -- the continuous counterpart of Data-Driven Computational Mechanics -- is a modern paradigm that enhances classical continuum mechanics by incorpor…
A regularized method for quadratic optimization problems with finite-dimensional degeneracy
C. G. Gebhardt, I. Romero
We propose and analyze a perturbative regularization method to approximate quadratic optimization problems with finite-dimensional degeneracy. The original problem is first approxi…
A variational multiscale approach to PDE-constrained optimization problems arising in Data-Driven Computational Mechanics
Ramon Codina, Roberto Federico Ausas, Pedro Balbão Bazon +1
We consider the primal and dual forms of the optimality conditions for PDE-contrained optimization problems arising in Data-Driven Computational Mechanics when specialized to the r…
On the use of an advanced Kirchhoff rod model to study mooring lines
Bruno A. Roccia, Hoa T. Nguyen, Petter Veseth +2
In this work, we investigate the application of an advanced nonlinear torsion- and shear-free Kirchhoff rod model, enhanced with a penalty-based barrier function (to simulate the s…