Neural Fields / Implicit Neural Representations

Tutorials and applied case studies for continuous-coordinate function approximators — MLPs with positional encodings, SIREN, multiplicative filter networks, NeRF and successors, conditioning, multi-resolution encodings, and continuous-depth models.

The full curriculum lives in TUTORIAL_MASTER_LIST.md. Bayesian extensions (probabilistic SIREN, Bayesian NeRF, Bayesian Neural Fields) live in ../bayesian_nns/ Part G.

Layout¶

• notebooks/A_foundations/ — MLP function approximation, spectral bias, positional / Fourier encodings, init & training dynamics
• notebooks/B_architectures/ — SIREN family, multiplicative filter networks, periodic / wavelet / Gabor INRs
• notebooks/C_volumetric_nerf/ — vanilla NeRF, mip-NeRF, Plenoxels, Gaussian Splatting, SDF / occupancy
• notebooks/D_conditioning/ — FiLM, Hyper-RFF, hypernetworks, meta-learning, latent-modulated INRs
• notebooks/E_spatial_encoding/ — Slepian, spherical harmonics, hashgrid (Instant NGP), tri-plane
• notebooks/F_continuous_depth/ — Neural ODE / CDE / SDE
• notebooks/G_loss_constraints/ — PINN, equivariance / conservation, sparsity / TV / boundary penalties
• notebooks/H_applied/ — signals & images, 3D scenes, scientific / geospatial fields

Companion lists¶

• Bayesian variants → ../bayesian_nns/TUTORIAL_MASTER_LIST.md
• Pure GPs (RFF, spectral kernels, harmonic features) → ../gaussian_processes/TUTORIAL_MASTER_LIST.md
• Normalizing flows (continuous-depth + invertible) → ../gaussianization/TUTORIAL_MASTER_LIST.md