Yufei Liu gave a talk entitled Bayesian Calibration of Steel Creep Model with ACBICI (with C. Schenk and I. Romero) at the Congress on Numerical Methods in Engineering (CMN 2026) in Gijón, Spain.

A. Kaleen gave a talk entitled Thermomechanical Simulations of Additive Manufacturing Processes with Temperature-Dependent Material Properties (with N. Rossi and I. Romero) at the Congress on Numerical Methods in Engineering (CMN 2026) in Gijón, Spain.

David Portillo gave a talk entitled A Flexible Coupling Strategy for Incompatible Meshes: A Structural-Arlequin Approach (with N. Rossi) at the Congress on Numerical Methods in Engineering (CMN 2026) in Gijón, Spain.

Jacobo Ayensa-Jiménez gave a talk entitled Rate-Independent Epigenetics: A thermodynamically consistent framework for epigenetic evolution at the International Workshop on Perspectives in Nonlinear Continuum Theories (eds. J. Merodio and R. Ogden), held in Castro Urdiales, Spain, June 29 – July 3, 2026. Co-author: I. Romero.

New article: Romero, I. and Ortiz, M. (2026). Identification of optimal history variables and corresponding hereditary laws in linear viscoelasticity, Computer Methods in Applied Mechanics and Engineering, 461, 119122. (link).

In this article, we develop an operator-theoretic formulation of linear hereditary constitutive models and characterize optimal finite-rank internal-variable approximations in the sense of Kolmogorov 𝑁-widths. The history operator is shown to be compact under natural assumptions on the relaxation kernel, thereby admitting optimal low-rank approximations. The resulting reduced models inherit thermodynamic consistency, stability, and provable approximation bounds. An analysis clarifies the structural relation between hereditary representations and internal-variable theories and provides a rigorous basis for reduced-order modelling in computational mechanics. Selected numerical examples showcase optimal convergence of approximations with respect to rank and sampling.

New article: Schenk, C. and Romero, I. (2026). A Framework for the Bayesian Calibration of Complex and Data-Scarce Models in Applied Sciences, Archives of Computational Methods in Engineering. (link).

In this review article, we present a unified framework for the Bayesian calibration of computational models, with particular emphasis on applications involving computationally expensive simulations and scarce experimental data. The article describes four calibration strategies of increasing complexity — covering simple and expensive models, with and without model discrepancy — and introduces ACBICI, a new open-source Python library that implements all of them. The library supports single- and multi-output calibration with Gaussian process surrogates, MCMC and variational inference, and provides practical guidelines for reliable Bayesian calibration in engineering and applied sciences.

New article: - Kaleem, A. and Gebhardt, C. and Romero, I. (2026). On the pure traction problem of linear elasticity: A regularized formulation and its robust approximation, Computer Methods in Applied Mechanics and Engineering, 459, 119105. link

An old-standing problem in elasticity and other variational problems is finding a solution when only Neumann (traction) boundary conditions are imposed on the solution domain. Traditionally, this very common problem has been solved by selecting some special nodes and imposing special boundary conditions on them to remove the rigid body motions. Alternatively, Lagrange multipliers can be employed for the same purpose, at a much higher cost.

On April 15, the 7th Doctoral Day will be held at IMDEA Materials. During this event, several aspects related to the research career at the institute will be presented. Attendees will also hear from a former researcher — now a PhD graduate — and will have the opportunity to connect with researchers who will present opportunities to begin this career path.

Registration is mandatory! See below.

New article: Romero, I. and Ortiz, M. (2026). A note on data-driven methods for mechanical problems with non-unique solutions, Meccanica, 61. (link).

In this article, we show that most of the usual machine learning models have a serious drawback when employed in nonlinear mechanics: they can not predict the bifurcation of solutions and therefore might incur in serious misrepresentations, even for the simplest problem.

New article: Portillo, D. and Romero, I. (2026). Embedding structures in continua: linear models and finite element discretizations, /Computer Methods in Applied Mechanics and Engineering/, 451, 118683. (link).

In this article, we present a /model/ for the embedding of arbitrary deformable structural models inside continua. Additionally, we introduce convergent finite element discretizations of the proposed model. Applications include: reinforced concrete, materials with inclusions, materials with fibers, etc.