{"ID":2873393,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06574","arxiv_id":"2509.06574","title":"Topological Regularization for Force Prediction in Active Particle Suspension with EGNN and Persistent Homology","abstract":"Capturing the dynamics of active particles, i.e., small self-propelled agents that both deform and are deformed by a fluid in which they move is a formidable problem as it requires coupling fine scale hydrodynamics with large scale collective effects. So we present a multi-scale framework that combines the three learning-driven tools to learn in concert within one pipeline. We use high-resolution Lattice Boltzmann snapshots of fluid velocity and particle stresses in a periodic box as input to the learning pipeline. the second step takes the morphology and positions orientations of particles to predict pairwise interaction forces between them with a E(2)-equivariant graph neural network that necessarily respect flat symmetries. Then, a physics-informed neural network further updates these local estimates by summing over them with a stress data using Fourier feature mappings and residual blocks that is additionally regularized with a topological term (introduced by persistent homology) to penalize unrealistically tangled or spurious connections. In concert, these stages deliver an holistic highly-data driven full force network prediction empathizing on the physical underpinnings together with emerging multi-scale structure typical for active matter.","short_abstract":"Capturing the dynamics of active particles, i.e., small self-propelled agents that both deform and are deformed by a fluid in which they move is a formidable problem as it requires coupling fine scale hydrodynamics with large scale collective effects. So we present a multi-scale framework that combines the three learni...","url_abs":"https://arxiv.org/abs/2509.06574","url_pdf":"https://arxiv.org/pdf/2509.06574v1","authors":"[\"Sadra Saremi\",\"Amirhossein Ahmadkhan Kordbacheh\"]","published":"2025-09-08T11:39:42Z","proceeding":"cond-mat.soft","tasks":"[\"cond-mat.soft\",\"cs.LG\"]","methods":"[\"Graph Neural Network\"]","has_code":false}