Create README.md

README.md

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+[[Project Page](https://ml-jku.github.io/UPT)]
+
+
+**U**niversal **P**hysics **T**ransformers (UPTs) are a novel learning paradigm that can model a wide range of 
+spatio-temporal problems - both for Lagrangian and Eulerian discretization schemes.
+
+
+<p align="center">
+<img width="100%" alt="mimrefiner_schematic" src="https://raw.githubusercontent.com/ml-jku/UPT/main/.github/imgs/schematic.png">
+</p>
+
+
+UPTs can model transient flow simulations (Eulerian discretization scheme) as indicated by test loss and rollout performance (measured via correlation time):
+
+<p align="center">
+<img width="50%" alt="maect_schematic" src="https://raw.githubusercontent.com/ml-jku/UPT/main/.github/imgs/cfd_scaling_testloss.svg">
+<img width="50%" alt="lowshot_vitl" src="https://raw.githubusercontent.com/ml-jku/UPT/main/.github/imgs/cfd_scaling_corrtime.svg">
+</p>
+
+
+<p align="center">
+<img width="100%" alt="mimrefiner_schematic" src="https://raw.githubusercontent.com/ml-jku/UPT/main/.github/imgs/cfd_rollout.png">
+</p>
+
+
+and also the flow-field of particle based simulations (Lagrangian discretization scheme):
+
+<p align="center">
+<img width="100%" alt="mimrefiner_schematic" src="https://raw.githubusercontent.com/ml-jku/UPT/main/.github/imgs/lagrangian_field.png">
+</p>