AI & ML
A characteristic mapping method for dissipative evolution equations
arXiv cs.NA0 views
arXiv:2611.08261v1 Announce Type: new Abstract: The Characteristic Mapping (CM) method is a transport scheme which discretizes the Lagrangian flow map as a composition of spatial interpolants. This adaptive approximation technique has demonstrated novel resolution properties and the ability to efficiently simulate the fine scales of inviscid turbulent flows. In this work, we construct an extension of this approach to the solution of dissipative problems, including the Navier-Stokes equations. The extension lifts a general scalar evolution equation to a path in a group of diffeomorphisms, where the semigroup approach of the CM method can be exploited. We give an interpretation of this lift through the geometric framework of optimal transport. We then introduce a semidirect product evolution technique to perform the time stepping of the map components. The map is evolved based on the semi-Lagrangian CM approach, and its log-Jacobian is evolved explicitly by an exponential integrator. The numerical scheme is demonstrated to be high-order accurate and is tested on a range of canonical nonlinear dissipative equations. By retaining the resolution properties of the CM method, the approach shows a promising ability to simulate turbulent fluid flows across all Reynolds numbers.
Source:https://arxiv.org/abs/2611.08261PreviousUniversal Dimension Formula for Planar $C^r$ Splines at Degree $3r+1$NextVariably saturated flow on polygonal and cut meshes using a mass-conservative hybrid virtual element
Related posts
Ask HN: Has anyone used LLMs to audit TrueCrypt for backdoors?
0Hacker News Ask
Ask HN: How can I get informed in the static vs. dynamic typing debate?
0Hacker News Ask
Show HN: Hackercasts – Podcasts Mentioned on HN
0Hacker News Show
Best AI tools to add vocals to your own beat (2026)
0dev.to

How to Keep Product Information in Sync Across a Multilingual Website
0dev.to
UK Warns Its Critical Infrastructure Faces Grave Risk
0Dark Reading