Interpretable and Structure-Preserving Data-Driven Methods for Physical Simulations

Interpretable and Structure-Preserving Data-Driven Methods for Physical Simulations

DataLearning@ICL via YouTube Direct link

Category of data-driven methods via level of intrusiveness

19 of 19

19 of 19

Category of data-driven methods via level of intrusiveness

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Interpretable and Structure-Preserving Data-Driven Methods for Physical Simulations

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Intro
  2. 2 Awesome reduced order model team and collaborators
  3. 3 Physical simulations play an important role in modern scienc
  4. 4 How does conditional generative adversarial network perform?
  5. 5 Pro and cons of black-box approach
  6. 6 How can we get an interpretability?
  7. 7 DMD accelerates 3D printing process simulation
  8. 8 Time-windowing Wavelet DMD improves accuracy
  9. 9 Are there other data-driven interpretable methods?
  10. 10 Parameterized latent space dynamics identification (LaSDI)
  11. 11 Performance of LaSDI to radial advection problem
  12. 12 gLaSDI: physics-informed greedy latent space dynamics identificat
  13. 13 How about physics-constrained model?
  14. 14 Projection-based linear subspace reduced order model
  15. 15 Space-time ROM achieves the maximal compression
  16. 16 Component-wise ROM accelerates lattice-structure design optir
  17. 17 PROM accelerates wind turbine blade design optimization
  18. 18 Database local ROMs accelerate multi-start airplane wing optin
  19. 19 Category of data-driven methods via level of intrusiveness

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.