2025/12/22 by Thi-Hoa Nguyen, Nguyen, Thi-Hoa, Viljar H. Gjerde +5
Computer Science · Mathematics · Physics and Astronomy · #Computational Engineering #FOS: Computer and information sciences #FOS: Mathematics #Finance #Model Reduction and Neural Networks #Optimization and Control (math.OC) #Stochastic Gradient Optimization Techniques #Tensor decomposition and applications #and Science (cs.CE)
paper · doi:10.48550/arxiv.2512.19912
openalex publication_date 2025/12/22 · openalex created_date 2025/12/25 · openalex updated_date 2026/07/28
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 nonlinear systems computed with multiple load steps. In particular, we combine the greedy optimization algorithm with the direct data-driven solver based on ADM which is firstly introduced in [2] and combined with the Newton-Raphson method for nonlinear elasticity in [3]. We numerically illustrate via one- and two-dimensional bar and truss structures exhibiting nonlinear strain measures and different constitutive datasets that our solving strategy generally achieves a better approximation of the globally optimal solution. This, however, comes at the expense of higher computational cost which is scaled by the number of "greedy" searches. Using this solving strategy, we reproduce the first cycle of the cyclic testing for a nylon rope that was performed at industrial testing facilities for mooring lines manufacturers. We also numerically illustrate for a truss structure that our solving strategy generally improves the accuracy and robustness in cases of an unsymmetrical data distribution and noisy data.