4 papers
Learning turbulent transport via Mori--Zwanzig graph neural networks
André Freitas, Xander M. de Wit, Alessandro Gabbana +4
We introduce a Mori--Zwanzig graph neural network (MZ--GNN) framework for learning reduced-order Lagrangian dynamics of tracer particles in homogeneous isotropic turbulence. The mo…
HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs
Jinpai Zhao, Nishant Panda, Yen Ting Lin +3
We introduce HyCOP, a modular framework that learns parametric PDE solution operators by composing simple modules (advection, diffusion, learned closures, boundary handling) in a q…
Are We Really Learning the Score Function? Reinterpreting Diffusion Models Through Wasserstein Gradient Flow Matching
An B. Vuong, Michael T. McCann, Javier E. Santos +1
Diffusion models are commonly interpreted as learning the score function, i.e., the gradient of the log-density of noisy data. However, this assumption implies that the target of l…
Discrete Spatial Diffusion: Intensity-Preserving Diffusion Modeling
Javier E. Santos, Agnese Marcato, Roman Colman +2
Generative diffusion models have achieved remarkable success in producing high-quality images. However, these models typically operate in continuous intensity spaces, diffusing ind…