Identifiability Limits of Physics-Informed Inference for Spatial Stochastic Dynamics from Static Snapshots
arXiv stat.ML4w4 min read
arXiv:2607.01749v1 Announce Type: cross Abstract: Despite increasing scale and resolution, many biological measurements remain destructive, revealing only spatial information rather than the dynamics it encodes. By combining flexible representations with mechanistic constraints, physics-informed machine learning offers a promising route to inferring these dynamics from static snapshots. Motivated by subcellular imaging of gene expression, we ask when a static spatial pattern of molecules can identify spatially varying diffusivity, creation, destruction, and boundary exchange, and how different
