Physics-informed NNs, Generative Physics and Astronomy

Aim: Generative AI with built-in physical consistency

Tasks:

• Develop and train models with new modalities for physical fields (e.g. symbolic and hybrid representations)

• Improve SOTA architectures (Neural Operators, NeuralODE, PINNs, MPP)

• Design algorithms for advanced data assimilation in PINNs, training stability (using RL), and better domain knowledge integration