The focus of our research group is directed to the interplay between mathematics, engineering, physics, and computational sciences, seeking to solve complex interdisciplinary problems in engineering and applied sciences. We are interested in both, basic research and applications, with special attention to the following topics:
  • Nonlinear Dynamics
  • Uncertainty Quantification
  • Inverse Problems
  • Reduced Order/Surrogate Modeling
  • Computational Science and Engineering
  • Industrial Mathematics
We work mainly in the qualitative and quantitative study of nonlinear phenomena and systems (epidemiological forecasting, damage detection, energy harvesters, etc.), seeking to better understand their underlying dynamic behavior. For this purpose we make use of sophisticated analytical, numerical and data-driven techniques, as well as hybrid approaches combining them. We are also interested in the development of state of art computational tools for the analysis of complex systems, such as numerical codes for ordinary/partial differential equations, reduced order/surrogate models, machine learning/statistical regressors, etc. In this context, our work is organized into the following interdisciplinary and transversal lines of research:
  • Nonlinear and chaotic phenomena in complex systems
  • Probabilistic modeling of uncertainties in nonlinear systems
  • Inverse problems for calibration of computational models
  • Reduction of complexity in high order computational models
  • Computational modeling of industrial problems