Beatriz Moya - Applied Mechanics and Bioengineering

PhD 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.

Education

M.Sc. in Industrial Engineering (Sept 2015–June 2017)

University of Zaragoza

Master’s thesis: Implementation study of thrust network analysis for vaulted structures.

Supervised by Prof E. Cueto.

Coded simulation program based on the Trust Network Analysis for optimized calculation of vaulted structures.

B. Sc. in Mechanical Engineering (Sept 2011-June 2015)

University of Zaragoza

Senior project: Lotus Meditation Assistive Device Using EEG to Measure State of Mind.

Supervised by Prof. Simpkins.

Meditation training device designed to read brain signals and provide real time feedback to help a person to meditate as part of the academic project for the design of assistive devices.

Research areas

Data-driven modelling
Phisically-based simulation
Model Order Reduction

Training

An ECCOMAS Advanced Online Course on Computational Structural Dynamics. Czech Academy of Sciences. September 2020.

The Art of Modeling in Computational Solid Mechanics. CISM – International Centre for Mechanical Sciences. October 2019.

Congress participation

Conference Talks

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.

Poster participation

C2D3 Virtual Symposium 2020. Cambridge, UK (On-line).Digital twins of fluid dynamics for realtime interaction.

Congress on Numerical Methods in Engineering 2019. Guimaraes (Portugal). Data-driven learning of slosh dynamics.

Workshop Talks

DataBEST 2019. Paris (France). Data-based manifold learning of slosh dynamics.

Publications

Orcid Number: 0000-0001-5483-6012

Link to Google Scholar Profile

Moya, B., Badías, A., Alfaro, I., Chinesta, F., & Cueto, E. (2020). Digital twins that learn and correct themselves. International Journal for Numerical Methods in Engineering. https://doi.org/10.1002/nme.6535
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. https://doi.org/10.1007/s00466-019-01705-3
Chinesta, F., Cueto, E., Grmela, M., Moya, B., & Pavelka, M. (2019). Learning Physics from Data: a Thermodynamic Interpretation. arXiv preprint arXiv:1909.01074.