This is the largest active area. It includes:
If you are developing a new numerical relativity algorithm, do not tag only 10.30. Add 10.50.-h (numerical methods for PDEs in physics) to ensure your work reaches applied mathematicians.
: General theory of fields and particles (e.g., string theory, symmetry breaking).
The hybrid field of physics-informed neural networks (PINNs), neural operators (DeepONet, FNO), and differentiable programming is the new frontier of PACS.10. These methods solve PDEs using deep learning architectures, merging classical numerical analysis with modern AI.
This is the largest active area. It includes:
If you are developing a new numerical relativity algorithm, do not tag only 10.30. Add 10.50.-h (numerical methods for PDEs in physics) to ensure your work reaches applied mathematicians.
: General theory of fields and particles (e.g., string theory, symmetry breaking).
The hybrid field of physics-informed neural networks (PINNs), neural operators (DeepONet, FNO), and differentiable programming is the new frontier of PACS.10. These methods solve PDEs using deep learning architectures, merging classical numerical analysis with modern AI.