v0.10.0

Heterogeneous-operator completion, matrix-free sparsity, ResDMD MVP,
classical DMD peers, and topology-blind VAMP-2 precursor. Homogeneous
defaults and the 0.9 RelGraph hetero path remain the baseline; shared-(d)
bit-compat is preserved where rectangular maps are not requested.

Added

• Windowed hetero fit, opt-in reverse-relation synthesis
  (synthesize_reverse_relations / synthesize_reverse_edge_types), and
  continuous hetero dynamics (dynamics_mode="continuous").
• Per-type latent widths (d_\tau) with rectangular relation maps (K_r);
  joint structural certificate on (\rho(K_{\mathrm{eff}})) (Gershgorin-style
  bound via stability_certificate, distinct from factor-wise proxies).
• Hetero parity for conformal UQ, GraphKoopmanEnv, hierarchical models
  (multiplex union-bank and typed per-type pooling; hetero fit uses
  hold_perm), and evaluate paths; hetero orbit ties
  (koopman_auto_orbits).
• Operator flag sparsity="distributed": matrix-free matvec path (Lanczos /
  Arnoldi spectrum with num_modes; multi-step Neumann / fixed-point
  inverse). Distinct from multi-GPU trainer extras.
• Finite-dictionary ResDMD MVP (koopman_graph.analysis.resdmd) and
  resolvent-norm grid (pseudospectra); tutorial
  examples/40_resdmd_pseudospectra.ipynb. Remains separate from
  diagnostic spectral_residuals.
• Bayesian Laplace UQ over operator factors (koopman_graph.uq.bayesian)
  and opt-in stochastic latent dynamics; Ulam-style transfer-operator
  baseline.
• Dask materialization helpers (koopman_graph.distributed.dask_prep,
  [dask] extra) — prep only; no Dask training loop.
• Classical DMD family peers: fbDMD, tlsDMD, optDMD, streaming DMD,
  depth-2 mrDMD; EDMD Nyström / random-feature approximations.
• Simplicial-1 / Hodge encode path (encoder="simplicial"); Tier A
  InvariantGeometryEncoder (rotation-invariant features; not equivariant
  (K)).
• Optional Tier B E3EquivariantEncoder via e3nn ([equivariance]
  extra): steerable encode to invariant scalar latents for linear (K)
  (still not an equivariant operator). Citations: Thomas2018TFN,
  Geiger2022e3nn.
• Topology-blind VAMP-2 precursor (vamp2_score / LossWeights.vamp2,
  optional [msm] deeptime oracles) — not GraphVAMPnets / MD toolchain.
• Sphinx: limitations 0.11 roadmap, capabilities / architecture / FAQ /
  API / tutorials updates for the 0.10 surface.

Notes

• Dense (N\cdot d) (or (\sum_\tau N_\tau d_\tau)) assembly ceilings still
  apply when spectrum / inverse require a dense effective matrix; the
  matrix-free path avoids assembly only under sparsity="distributed".
• ResDMD MVP is finite-dictionary only (Colbrook & Townsend / related
  residual DMD literature; keys ColbrookTownsend2023ResDMD,
  Colbrook2023ResidualDMD). Do not treat spectral_residuals as ResDMD.
• VAMP-2 precursor cites Wu & Noé (Wu2020VAMP) and Mardt et al.
  (Mardt2018VAMPnets); it is not a GraphVAMPnets or MSM production stack.
• 0.11 targets (high level): variable node cardinality / churn, directed
  hypergraph incidence modes, public cross-topology transfer API, sheaf /
  cell TDL, GraphVAMPnets + MD/MSM toolchains, Ray Train as model DDP,
  isotypic symmetry / equivariant (K).