Emulator differentiability

The learned emulator differentiates its uniform sticky-sphere mechanics and graph diffusion so their gradients compose with the GNS.

Frozen topology within each correction

Each learned update may be followed by mech_steps mechanics calls and diff_steps diffusion calls; zero selects the identity path for that correction. dt_mech and dt_diff set their respective explicit step sizes.

Pair discovery occurs outside autodiff. Topology is rebuilt between correction calls as the state evolves, then held fixed while that call is differentiated. Gradients cover continuous force, flux, and state arithmetic; neighbor topology remains a discrete forward input. Learned graphs use a host scipy.spatial.cKDTree; prescribed emulator corrections use a Warp HashGrid. Candidate and output work depends on local density and can be quadratic for dense states.

Torch Tape and JAX custom VJP

waxmorph.torch.warp_autograd wraps each mechanics or diffusion call in a torch.autograd.Function with a fresh warp.Tape. Backward seeds the output adjoint and replays the recorded emulator kernels. The mechanics bridge exposes position gradients; the diffusion bridge exposes concentration gradients.

waxmorph.jax.warp_autograd uses custom VJPs on CUDA, backed by dedicated backward-enabled per-pair Warp FFI kernels. JAX owns endpoint gathers, padding masks, scatter-adds, and state updates. Its mechanics bridge differentiates positions and radii on the frozen pairs; diffusion differentiates concentrations. Adding a correction therefore requires backend-specific bridge work.

Simulator derivative implementations

The forward simulator provides its own derivative implementations. mech_step_sticky uses explicit pair derivatives. mech_step_sticky_implicit uses local warp.grad() evaluation for polarity, thickness, mesenchymal alignment, and optional WNT terms; its soft-sphere derivatives remain explicit. Both paths use atomic reductions, so floating-point reduction order may vary on parallel devices.