Tootfinder

Effective Theories of Redshift-Space Galaxy Peculiar Velocities
Shi-Fan Chen, Cullan Howlett, Yan Lai, Fei Qin
https://arxiv.org/abs/2508.00066 https://arx…

Effective Theories of Redshift-Space Galaxy Peculiar Velocities
We present predictions for redshift-space peculiar velocity statistics in the Lagrangian and Eulerian formulations of the effective field theory (EFT) of large-scale structure. We compute 2-point pairwise velocity statistics up to the second moment at next-to-leading (1-loop) order, showing that they can be modeled together with redshift-space galaxy densities with a consistent set of EFT coefficients. We show that peculiar velocity statistics have a distinct dependence on long-wavelength bulk …

Bayesian nonparametric clustering for spatio-temporal data, with an application to air pollution
Luca Aiello, Raffaele Argiento, Sirio Legramanti, Lucia Paci
https://arxiv.org/abs/2505.24694

Bayesian nonparametric clustering for spatio-temporal data, with an application to air pollution
Air pollution is a major global health hazard, with fine particulate matter (PM10) linked to severe respiratory and cardiovascular diseases. Hence, analyzing and clustering spatio-temporal air quality data is crucial for understanding pollution dynamics and guiding policy interventions. This work provides a review of Bayesian nonparametric clustering methods, with a particular focus on their application to spatio-temporal data, which are ubiquitous in environmental sciences. We first introduce …

Quasi-optimality of the Crouzeix-Raviart FEM for p-Laplace-type problems
Johannes Storn
https://arxiv.org/abs/2507.12742 https://arxi…

Quasi-optimality of the Crouzeix-Raviart FEM for p-Laplace-type problems
We verify quasi-optimality of the Crouzeix-Raviart FEM for nonlinear problems of $p$-Laplace type. More precisely, we show that the error of the Crouzeix-Raviart FEM with respect to a quasi-norm is bounded from above by a uniformly bounded constant times the best-approximation error plus a data oscillation term. As a byproduct, we verify a novel more localized a priori error estimate for the conforming lowest-order Lagrange FEM.

Fluid Simulation on Vortex Particle Flow Maps
Sinan Wang, Junwei Zhou, Fan Feng, Zhiqi Li, Yuchen Sun, Duowen Chen, Greg Turk, Bo Zhu
https://arxiv.org/abs/2505.21946

Fluid Simulation on Vortex Particle Flow Maps
We propose the Vortex Particle Flow Map (VPFM) method to simulate incompressible flow with complex vortical evolution in the presence of dynamic solid boundaries. The core insight of our approach is that vorticity is an ideal quantity for evolution on particle flow maps, enabling significantly longer flow map distances compared to other fluid quantities like velocity or impulse. To achieve this goal, we developed a hybrid Eulerian-Lagrangian representation that evolves vorticity and flow map qu…

Tree-based adaptive finite element methods for deformable image registration
Nicol\'as A. Barnafi, Alberto F. Mart{\i}n, Ricardo Ruiz-Baier
https://arxiv.org/abs/2506.15876

Tree-based adaptive finite element methods for deformable image registration
In this work we propose an adaptive Finite Element Method (FEM) formulation for the Deformable Image Registration problem (DIR) together with a residual-based a posteriori error estimator, whose efficiency and reliability are theoretically established. This estimator is used to guide Adaptive Mesh Refinement and coarsening (AMR). The nonlinear Euler-Lagrange equations associated with the minimisation of the relevant functional are solved with a pseudo time-stepping fixed-point scheme which is f…