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From the 1 of 10 papers with an AI index.

most citedSurrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach

1 citations

10 papers

math.GR2026

Bounded cohomology, quotient extensions, and hierarchical hyperbolicity

Francesco Fournier-Facio, Giorgio Mangioni, Alessandro Sisto

The authors introduce bounded central extensions (those whose Euler class is represented by a bounded cocycle) and prove that such extensions of hierarchically hyperbolic groups (H…

math.OC2026

Semi-discrete convex order and Laguerre tessellation fitting

David P. Bourne, Thomas Gallouët, Quentin Mérigot +1

Laguerre tessellations offer an efficient way to parameterize a large class of convex partitions of Euclidean space using only a set of points and scalar weights. For this reason,…

stat.ME2026

A Distributed Plug-and-Play MCMC Algorithm for High-Dimensional Inverse Problems

Maxime Bouton, Pierre-Antoine Thouvenin, Audrey Repetti +1

Markov Chain Monte Carlo (MCMC) algorithms are standard approaches to solve imaging inverse problems and quantify estimation uncertainties, a key requirement in absence of ground-t…

cs.LG20261 cited

Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach

Nathalie C. Pinheiro, Donghu Guo, Hannah P. Menke +4

Modelling rock-fluid interaction requires solving a set of partial differential equations (PDEs) to predict the flow behaviour and the reactions of the fluid with the rock on the i…

math.AP2026

Semi-discrete optimal transport techniques for the compressible semi-geostrophic equations

David P. Bourne, Charlie P. Egan, Théo Lavier +1

We prove existence of weak solutions of the 3D compressible semi-geostrophic (SG) equations with compactly supported measure-valued initial data. These equations model large-scale…

cs.CL2026

TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning

Xiaosong Han, Ke Chen, Xindi Dai +7

In real-world deployment, LLMs are often adapted continually across tasks to keep LLMs up-to-date in production, where new fine-tuning should preserve previously learned skills. Ho…