BEATRIZ MOYA

 

Education

PhD in Mechanical Engineering (February 2018–June 2022)

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

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

 

Research areas

  • Data-driven modeling and machine intelligence
  • Physically-based simulation
  • Model Order Reduction
  • Digital and hybrid twins

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
 
  • Moya, B., Badías, A., González, D., Chinesta, F., & Cueto, E. (2022). Physics-informed Reinforcement Learning for Perception and Reasoning about Fluids. https://arxiv.org/pdf/2203.05775.pdf
  • Moya, B., Badías, A., González, D., Chinesta, F., & Cueto, E. (2021). Physics perception in sloshing scenes with guaranteed thermodynamic consistency. IEEE Transactions in Pattern Analysis and Machine Intelligence. https://arxiv.org/abs/2106.13301
  • 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. In: Barbaresco, F., Nielsen, F. (eds) Geometric Structures of Statistical Physics, Information Geometry, and Learning. SPIGL 2020. Springer Proceedings in Mathematics & Statistics, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-030-77957-3_14