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The SMG-Yau-Yau Filter and Lossless Data Assimilation: Resolving Infinite Lie Algebras via Statistical Fiber Theory

Abstract

Classical continuous-time non-linear filtering fails in generic non-linear state spaces due to an infinite Lie algebraic derivative explosion (dim(ℰ) = ∞), leading to filter divergence. To resolve this four-decade crisis, we introduce the SMG-Yau-Yau filter and lossless data assimilation framework built on Statistical Fiber Theory over an Orlicz manifold ℳ. By equipping ℳ with a Riemannian submersion and an Ehresmann connection, the unconstrained Duncan-Mortensen-Zakai score velocity field is orthogonally decomposed into Statistically Verifiable Directions (SVDχf) and Structural Internal Directions (SIDf). System non-linearities and unclosed Lie commutators are orthogonally quarantined in SIDf, protecting macroscopic base parameters from spatial derivative pollution while preserving total score variance energy. We unify the asymptotics through a Dual-Axis Collapse Mechanism: proving our model identically recovers classical Yau-Yau dynamics when dim(ℰ) < ∞, while large-sample limits (N → ∞) induce a thermodynamic quench that flattens infinite-dimensional geometry under dim(ℰ) = ∞. Finally, we formulate the Active Acausal Tension (AAT) functional to monitor accumulated model misspecification online. Exceeding a topological capacity threshold triggers Gauge Symmetry Breaking (GSB), which dynamically expands base coordinates (d → d+1) to ensure non-asymptotic stability and convergence to the exact state density.

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BibTeX
@misc{cheng2026smgyauyau,
  title = {{The SMG-Yau-Yau Filter and Lossless Data Assimilation: Resolving Infinite Lie Algebras via Statistical Fiber Theory}},
  author = {Bing Cheng and Yi-Shuai Niu and Howell Tong and Shing-Tung Yau},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.28062},
  eprint = {2609.28062},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.28062},
}
RIS
TY  - UNPB
TI  - The SMG-Yau-Yau Filter and Lossless Data Assimilation: Resolving Infinite Lie Algebras via Statistical Fiber Theory
AU  - Bing Cheng
AU  - Yi-Shuai Niu
AU  - Howell Tong
AU  - Shing-Tung Yau
PY  - 2026
DA  - 2026/09/23
PB  - arXiv
DO  - 10.48550/arxiv.2609.28062
UR  - https://arxiv.org/abs/2609.28062
AB  - Classical continuous-time non-linear filtering fails in generic non-linear state spaces due to an infinite Lie algebraic derivative explosion (dim(ℰ) = ∞), leading to filter divergence. To resolve this four-decade crisis, we introduce the SMG-Yau-Yau filter and lossless data assimilation framework built on Statistical Fiber Theory over an Orlicz manifold ℳ. By equipping ℳ with a Riemannian submersion and an Ehresmann connection, the unconstrained Duncan-Mortensen-Zakai score velocity field is orthogonally decomposed into Statistically Verifiable Directions (SVDχ_f) and Structural Internal Directions (SID_f). System non-linearities and unclosed Lie commutators are orthogonally quarantined in SID_f, protecting macroscopic base parameters from spatial derivative pollution while preserving total score variance energy. We unify the asymptotics through a Dual-Axis Collapse Mechanism: proving our model identically recovers classical Yau-Yau dynamics when dim(ℰ) < ∞, while large-sample limits (N → ∞) induce a thermodynamic quench that flattens infinite-dimensional geometry under dim(ℰ) = ∞. Finally, we formulate the Active Acausal Tension (AAT) functional to monitor accumulated model misspecification online. Exceeding a topological capacity threshold triggers Gauge Symmetry Breaking (GSB), which dynamically expands base coordinates (d → d+1) to ensure non-asymptotic stability and convergence to the exact state density.
ER  -