Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture.

ABSTRACT: The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations b

SUBMITTER: Comlekoglu T 

PROVIDER: S-EPMC12611147 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature