Tootfinder

Stationarity preserving nodal Finite Element methods for multi-dimensional linear hyperbolic balance laws via a Global Flux quadrature formulation
Wasilij Barsukow, Mario Ricchiuto, Davide Torlo
https://arxiv.org/abs/2510.02928

Stationarity preserving nodal Finite Element methods for multi-dimensional linear hyperbolic balance laws via a Global Flux quadrature formulation
We consider linear, hyperbolic systems of balance laws in several space dimensions. They possess non-trivial steady states, which result from the equilibrium between derivatives of the unknowns in different directions, and the sources. Standard numerical methods fail to account for this equilibrium, and include stabilization that destroys it. This manifests itself in a diffusion of states that are sup- posed to remain stationary. We derive new stabilized high-order Finite Element methods based …

Localizing the Ising CFT from the ground state of the Ising model on the fuzzy sphere
Kay Joerg Wiese
https://arxiv.org/abs/2510.09482 https://arxiv.org/pd…

Localizing the Ising CFT from the ground state of the Ising model on the fuzzy sphere
We locate the phase-transition line for the Ising model on the fuzzy sphere from a finite-size scaling analysis of its ground-state energy. This is similar to what was used to locate the complex CFT of the 5-state Potts model in dimension $d=2$ [PRL 133 (2024) 077101]. There it was shown that a CFT is characterized by a stationarity condition for the measured effective central charge. Our strategy is to write the ground-state energy as $E_{\rm GS}(N)/N_m = E_{0} + E_1 /N_m + E_{3/2}/N_m^{3/2}+ …

Sliding-Window Signatures for Time Series: Application to Electricity Demand Forecasting
Nina Drobac (LPSM), Margaux Br\'eg\`ere (LPSM), Joseph de Vilmarest (LPSM), Olivier Wintenberger (LPSM)
https://arxiv.org/abs/2510.12337

Sliding-Window Signatures for Time Series: Application to Electricity Demand Forecasting
Nonlinear and delayed effects of covariates often render time series forecasting challenging. To this end, we propose a novel forecasting framework based on ridge regression with signature features calculated on sliding windows. These features capture complex temporal dynamics without relying on learned or hand-crafted representations. Focusing on the discrete-time setting, we establish theoretical guarantees, namely universality of approximation and stationarity of signatures. We introduce an …

Network Topology and Information Efficiency of Multi-Agent Systems: Study based on MARL
Xinren Zhang, Sixi Cheng, Zixin Zhong, Jiadong Yu
https://arxiv.org/abs/2510.07888 https:…

Network Topology and Information Efficiency of Multi-Agent Systems: Study based on MARL
Multi-agent systems (MAS) solve complex problems through coordinated autonomous entities with individual decision-making capabilities. While Multi-Agent Reinforcement Learning (MARL) enables these agents to learn intelligent strategies, it faces challenges of non-stationarity and partial observability. Communications among agents offer a solution, but questions remain about its optimal structure and evaluation. This paper explores two underexamined aspects: communication topology and informatio…

Spatial Gaussian fields for complex areas with application to marine megafauna conservation
Martina Le-Bert Heyl, Janet van Niekerk, Haavard Rue
https://arxiv.org/abs/2510.00611

Spatial Gaussian fields for complex areas with application to marine megafauna conservation
Spatial Gaussian fields (SGFs) are widely employed in modeling the distributions of marine megafauna, yet they traditionally rely on assumptions of isotropy and stationarity, conditions that often prove unrealistic in complex ecological environments featuring coastlines, islands, and depth gradients acting as partial movement barriers. Existing spatial models typically treat these barriers as either fully impermeable, completely blocking species movement and dispersal, or entirely absent, which…