feat: add G-FNO (Group Equivariant Fourier Neural Operator) model

[lkyu-ly]

• Add core model package ppcfd/models/g_fno/ with FNO, GCNN, GFNO, Ghybrid, radialNO 2D/3D variants
• Migrate training scripts, data generation tools and README to examples/G-FNO/
• Register g_fno as optional dependency in ppcfd/models/init.py

forward diff

Since the original model did not release pre-trained weights, forward precision alignment was performed using randomly initialized model weights.

model_config={'num_channels': 1, 'initial_step': 10, 'modes': 12, 'width': 10, 'reflection': False, 'grid_type': 'symmetric'}
input_shape=(2, 64, 64, 10, 1)
torch_output_shape=(2, 64, 64, 1, 1)
paddle_output_shape=(2, 64, 64, 1, 1)
torch_mean=-1.874004304409e-01
torch_std=4.745460674167e-02
paddle_mean=-1.874004602432e-01
paddle_std=4.745460301638e-02
mean_abs_error=6.605296221096e-08
max_abs_error=3.427267074585e-07
>>> mean_rel_error=3.910871670800e-07
max_rel_error=1.200105609294e-05