> Physics simulation
> 1. Solution as a neural network
> 2. Dynamics as a neural network
> Digression: "hard" simulation in ML
> 3. Everything as a neural network
> Digression: neural operators
> Wrapping up and conclusive notes
> Takeaways
PINNs

Great in low data regime and continous solution,

but single use, known to be hard to train and needs the ground truth eq.

Neural-ODE

Generalize to new initial condition, and simple integration of physics knowledge

but hard to scale up.

End-to-End

Most powerful and flexible approach, good generalization capabilities.

but very data-hungry and lack of interpretability

>Thank you !