CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes
Saqur, R Deshpande, A (18 Sep 2020)
Reward Learning using Structural Motifs in Inverse Reinforcement Learning
Saqur, R (25 Sep 2022)
Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset
Naeini, S Saqur, R Saeidi, M Giorgi, J Taati, B (19 Jun 2023)
L(u)PIN: LLM-based Political Ideology Nowcasting
Kato, K Purnomo, A Cochrane, C Saqur, R (12 May 2024)
NIFTY Financial News Headlines Dataset
Saqur, R Kato, K Vinden, N Rudzicz, F (15 May 2024)
What Teaches Robots to Walk, Teaches Them to Trade too -- Regime Adaptive Execution using Informed Data and LLMs
Saqur, R (19 Jun 2024)
Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework
Vinden, N Saqur, R Zhu, Z Rudzicz, F (21 Feb 2025)
Thu, 05 Feb 2026

12:00 - 13:00
C5

Well-Posedness of Characteristic Free-Boundary Problems in Ideal Compressible MHD

Difan Yuan
(Beijing Normal University)
Abstract

We study two-dimensional characteristic free-boundary problems in ideal compressible magnetohydrodynamics. For current-vortex sheets, surface-wave effects yield derivative loss and only weak (neutral) stability; under a sufficient stability condition on the background state we obtain anisotropic weighted Sobolev energy estimates and prove local-in-time existence and nonlinear stability via a Nash-Moser scheme, confirming stabilization by strong magnetic fields against Kelvin-Helmholtz instability. For the plasma-vacuum interface, coupling hyperbolic MHD with elliptic pre-Maxwell dynamics, we establish local existence and uniqueness provided at least one magnetic field is nonzero along the initial interface.


 

Large-order perturbation theory of linear eigenvalue problems
Chapman, J Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Multimodal graph networks for compositional generalization in visual question answering
Saqur, R Narasimhan, K Advances in Neural Information Processing Systems volume 2020-December (01 Jan 2020)
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