ICML ’24 is over, but for all those who didn’t have a chance to enjoy and study our workshop submission in more detail – this is your chance. Below you can find all five workshop submissions in their full glory. If any questions come up, feel free to contact us, of course!
Our works covered a large ground in scientific machine learning, i.e., combinations of numerical simulations and deep learning techniques. In summary, we covered:
- Flow matching and diffusion models for inverse problems with physics constraints
- Higher-order differentiable Navier-Stokes solvers
- SE(3) Equivariance in graph Neural Networks for fluid flows
- Autoregressive diffusion models for turbulent flow simulations
- Diffusion models for posterior sampling and uncertainty quantification of airfoil flows
You can follow each of the links to view the full poster. Enjoy!
