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