Ph. D. candidate
Beatriz is a third year PhD student at University of Zaragoza, at the Mechanical Engineering Department. She holds a B.S. in Mechanical Engineering and a M. Eng. in Industrial Engineering, both obtained from the University of Zaragoza, where she started her research career. Her thesis is oriented towards data-based modeling for real-time simulation and the development of digital twins and mixed reality applications.
M.Sc. in Industrial Engineering (Sept 2015–June 2017)
B. Sc. in Mechanical Engineering (Sept 2011-June 2015)
- Data-driven modelling
- Phisically-based simulation
- Model Order Reduction
The Art of Modeling in Computational Solid Mechanics. CISM – International Centre for Mechanical Sciences. October 2019.
Coupled Problems 2019. Sitges (Spain). Data-driven, reduced-order modeling and simulation of free-surface flows.
Congress on Numerical Methods in Engineering 2019. Guimaraes (Portugal). Data-driven learning of slosh dynamics.
ECCOMAS Young Investigators Conference 2019. Krakow(Poland). Manifold learning of complex fluid behavior for real-time simulation.
DataBEST 2019. Paris (France). Data-based manifold learning of slosh dynamics.
- Moya B, Alfaro I, Gonzalez D, Chinesta F, Cueto E (2020) Physically sound, self-learning digital twins for sloshing fluids . PLOS ONE 15(6): e0234569.https://doi.org/10.1371/journal.pone.0234569
- Moya, B., González, D., Alfaro, I., Chinesta, F., & Cueto, E. (2019). Learning slosh dynamics by means of data. Computational Mechanics, 64(2), 511-523.
- Chinesta, F., Cueto, E., Grmela, M., Moya, B., & Pavelka, M. (2019). Learning Physics from Data: a Thermodynamic Interpretation. arXiv preprint arXiv:1909.01074.