BibTeX

@article{2507.22256v1,
Author        = {Jun Won Park and Kangyu Zhao and Sanket Rane},
Title         = {Spatiodynamic inference using vision-based generative modelling},
Eprint        = {2507.22256v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {q-bio.QM},
Abstract      = {Biological systems commonly exhibit complex spatiotemporal patterns whose
underlying generative mechanisms pose a significant analytical challenge.
Traditional approaches to spatiodynamic inference rely on dimensionality
reduction through summary statistics, which sacrifice complexity and
interdependent structure intrinsic to these data in favor of parameter
identifiability. This imposes a fundamental constraint on reliably extracting
mechanistic insights from spatiotemporal data, highlighting the need for
analytical frameworks that preserve the full richness of these dynamical
systems. To address this, we developed a simulation-based inference framework
that employs vision transformer-driven variational encoding to generate compact
representations of the data, exploiting the inherent contextual dependencies.
These representations are subsequently integrated into a likelihood-free
Bayesian approach for parameter inference. The central idea is to construct a
fine-grained, structured mesh of latent representations from simulated dynamics
through systematic exploration of the parameter space. This encoded mesh of
latent embeddings then serves as a reference map for retrieving parameter
values that correspond to observed data. By integrating generative modeling
with Bayesian principles, our approach provides a unified inference framework
to identify both spatial and temporal patterns that manifest in multivariate
dynamical systems.},
Year          = {2025},
Month         = {Jul},
Url           = {http://arxiv.org/abs/2507.22256v1},
File          = {2507.22256v1.pdf}
}