Explicit Monotone Stable Super-Time-Stepping Methods for Finite Time Singularities
Zheng Tan, Tariq D. Aslam, Andrea L. Bertozzi

TL;DR
This paper introduces explicit super-time-stepping methods, RKL and RKG, for efficiently resolving finite-time singularities in nonlinear PDEs, offering stability, accuracy, and computational advantages over traditional implicit methods.
Contribution
The paper develops and analyzes explicit RKL and RKG super-time-stepping methods that are monotone and stable, specifically tailored for resolving finite-time singularities in nonlinear PDEs.
Findings
RKL and RKG methods achieve smaller run times compared to implicit methods.
Numerical monotonicity is proven for RKL and RKG under certain conditions.
Methods maintain accuracy while resolving singularity scales without smaller timesteps.
Abstract
We explore a novel way to numerically resolve the scaling behavior of finite-time singularities in solutions of nonlinear parabolic PDEs. The Runge--Kutta--Legendre (RKL) and Runge--Kutta--Gegenbauer (RKG) super-time-stepping methods were originally developed for nonlinear complex physics problems with diffusion. These are multi-stage single step second-order, forward-in-time methods with no implicit solves. The advantage is that the timestep size for stability scales with stage number as . Many interesting nonlinear PDEs have finite-time singularities, and the presence of diffusion often limits one to using implicit or semi-implicit timestep methods for stability constraints. Finite-time singularities are particularly challenging due to the large range of scales that one desires to resolve, often with adaptive spatial grids and adaptive timesteps. Here we show two…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNumerical methods for differential equations · Iterative Learning Control Systems · Advanced Numerical Methods in Computational Mathematics
