Physics-Constrained Correlation-Aware Attention for Collective Cell Dynamics

Dmitriy Stanislavchuk-Abovsky, Andrei Zakharov, Ilya Makarov

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Track: tiny / short paper (2-4 pages excluding references; extended abstract format)

Keywords: Physics-informed attention, Graph neural networks, Collective cell migration, Correlation-based priors

TL;DR: Physics-guided attention mechanisms improve the interpretability and robustness of graph-based models for collective cell migration

Abstract: Collective cell migration is shaped by short-range physical interactions between neighboring cells, but trajectory predictors often rely on heuristic attention that lacks physical grounding and interpretability. We introduce a physics-constrained, correlation-aware attention framework that embeds analytically extracted direct interaction priors into graph-based models of cell dynamics. Our approach estimates the direct correlation function from empirical structure factors using Ornstein–Zernike-based deconvolution in Fourier space and incorporates this short-range signal into attention logits as a physical prior. To encourage consistent use of this prior, we propose a variational alignment objective that regularizes learned attention distributions toward physically motivated interaction patterns via a KL divergence. This framework yields physically meaningful attention representations and provides a principled way for integrating statistical physics into representation learning for biological dynamics. We present preliminary qualitative results on collective cell migration data and outline directions for systematic evaluation and extension.

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Submission Number: 85