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Dr.-Ing. Mauricio Fernández


Room 3.137
Pfaffenwaldring 7
70569 Stuttgart

Tel.: +49 (0) 711 / 685 - 66341


Research field

My research interests are material modelling, homogenization theory, data-supported materials design of multiphase materials, neural networks and objective-oriented model order reduction.

In the field of materials design my aim is, based on (e.g., mechanical, thermo-mechanical, piezomechanical) materials properties prescribed/required by an application, (1) to scan large material databases for potential candidates or combinations and (2) to determine optimal microstructure statistics.

In the field of neural networks, I mainly work in Python with Numpy, Keras and TensorFlow and train the neural networks based on large simulation datasets.

Further, I work on objective-oriented model order reduction techniques, which update a quantity of interest based on suitable function spaces, data-driven surrogate models and error estimates.


[1] Thomas Böhlke and Mauricio Lobos. Representation of Hashin–Shtrikman bounds of cubic crystal aggregates in terms of texture coefficients with application in materials design. Acta Materialia, 67:324--334, apr 2014. [ DOI | http ]
[2] Alexander Fidlin and Mauricio Lobos. On the limiting of vibration amplitudes by a sequential friction-spring element. Journal of Sound and Vibration, 333(23):5970--5979, nov 2014. [ DOI | http ]
[3] Mauricio Lobos and Thomas Böhlke. Bounds and an isotropically self-consistent singular approximation of the linear elastic properties of cubic crystal aggregates for application in materials design. PAMM, 14(1):533--534, dec 2014. [ DOI | http ]
[4] Mauricio Lobos and Thomas Böhlke. Materials design for the anisotropic linear elastic properties of textured cubic crystal aggregates using zeroth-, first- and second-order bounds. International Journal of Mechanics and Materials in Design, 11(1):59--78, mar 2015. [ DOI | http ]
[5] Mauricio Lobos, Tunc Yuzbasioglu, and Thomas Böhlke. Materials design of elastic properties of multiphase polycrystalline composites using model functions. PAMM, 15(1):459--460, oct 2015. [ DOI | http ]
[6] Mauricio Lobos, Tunc Yuzbasioglu, and Thomas Böhlke. Robust materials design of anisotropic elastic properties of polycrystalline composites. Conference Proceedings of the YIC GACM 2015, pages 158--161, 2015.
[7] Mauricio Lobos and Thomas Böhlke. On optimal zeroth-order bounds of linear elastic properties of multiphase materials and application in materials design. International Journal of Solids and Structures, 84:40--48, may 2016. [ DOI | http ]
[8] Ludovic Noels, Ling Wu, Laurent Adam, Jan Seyfarth, Ganesh Soni, Javier Segurado, Gottfried Laschet, Geng Chen, Maxime Lesueur, Mauricio Lobos, Thomas Böhlke, Thomas Reiter, Stefan Oberpeilsteiner, Dietmar Salaberger, Dieter Weichert, and Christoph Broeckmann. Effective Properties. In Ulrich Prahl Georg J. Schmitz, editor, Handbook of Software Solutions for ICME, chapter 6, pages 433--485. Wiley-VCH Verlag GmbH & Co., 2016. [ DOI ]
[9] Mauricio Lobos, Tunc Yuzbasioglu, and Thomas Böhlke. Homogenization and Materials Design of Anisotropic Multiphase Linear Elastic Materials Using Central Model Functions. Journal of Elasticity, 128(1):17--60, jun 2017. [ DOI | http ]
[10] Mauricio Lobos Fernández. Homogenization and materials design of mechanical properties of textured materials based on zeroth-, first- and second-order bounds of linear behavior. Doctoral thesis, Karlsruhe Institute of Technology, 2018. [ DOI | http ]
[11] Mauricio Lobos Fernández and Thomas Böhlke. Representation of Hashin–Shtrikman Bounds in Terms of Texture Coefficients for Arbitrarily Anisotropic Polycrystalline Materials. Journal of Elasticity, 134:1--38, january 2019. [ DOI | http ]
[12] Mauricio Fernández and Thomas Böhlke. Hashin-Shtrikman bounds with eigenfields in terms of texture coefficients for polycrystalline materials. Acta Materialia, february 2019. [ DOI | http ]
[13] Felix Fritzen, Mauricio Fernández and Fredrik Larsson. On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling. Frontierts in Materials, april 2019. [ DOI | http ]