The paper introduces Weak-form Anchored Rollout for PDEs (WARP), a method that learns PDE solvers by differentiating through multistep rollouts anchored at observed states. WARP addresses noise sensitivity and data consistency issues of sparse regression approaches, producing numerically stable rollouts and improved coefficient recovery across eight fluid and plasma benchmarks, including high‑dimensional turbulent simulations. The method is tailored to systems solvable by spectral Galerkin techniques and outperforms a single‑step weak‑form SINDy baseline in rollout accuracy and statistical metrics.
By Mellow Baixuan Chen, Alan A. Kaptanoglu
arXiv:2609.37609v1 Announce Type: new
Abstract: Magnetohydrodynamics (MHD) is central to plasma modeling in astrophysics, space science, fusion, and engineering, but resolving multiscale MHD dynamics...
By Radhika Achikanath Chirakkara, Rajdeep Haldar, Zezheng Song, Jiequn Han
arXiv:2606. 26592v1 Announce Type: cross Abstract: We propose latent-space diffusion posterior sampling (L-DPS), an approximate Bayesian framework for high-dimensional inverse problems governed by partial differential equations (PDEs).
By Yuanzhe Wang, Alexandre M. Tartakovsky
arXiv:2606. 00349v1 Announce Type: cross Abstract: Reconstructing spatiotemporal fields from partial observations is fundamental to scientific inference, from inferring atmospheric states from satellite data to recovering fluid states from imaging.
By Xianwei Zou, Sheikh Md Shakeel Hassan, Arthur Feeney, Aparna Chandramowlishwaran
arXiv:2606. 15512v1 Announce Type: new Abstract: Real-time reconstruction of magnetohydrodynamic equilibria is essential for plasma shaping, stability assessment and feedback control in magnetic confinement fusion.
By Jay Phil Yoo, William Howes, Yashika Ghai, Kazuma Kobayashi, Souvik Chakraborty, Syed Bahauddin Alam
arXiv:2511. 22486v3 Announce Type: replace-cross Abstract: The requirement for large-scale global simulations of plasma is an ongoing challenge in both space and laboratory plasma physics.
By Samuel Burles, Enrico Camporeale