Integrating physics priors into machine learning enhances efficiency, reduces data needs and yields reliable results. This Perspective explores physics-driven learning and inverse modelling of generative models to provide solutions for inverse problem in quantum chromodynamics.
- Gert Aarts
- Kenji Fukushima
- Kai Zhou
