arXiv:2608.29069v1 Announce Type: cross
Abstract: Operator-learning surrogates have been benchmarked largely on single-field, single-interface problems, leaving unclear whether architectural choices...
By Muhammad Abid, Arth Sojitra, Omer San
arXiv:2608. 06428v1 Announce Type: new Abstract: Deep Operator Networks (DeepONets; arXiv:1910.
By Khemraj Shukla, George Em Karniadakis
arXiv:2606. 17460v1 Announce Type: new Abstract: Neural operators are widely used as surrogate solution maps for partial differential equations (PDEs), but full-size models can be costly to store, deploy, and evaluate in many-query scientific workflows.
By Lennon J. Shikhman
arXiv:2608. 13629v1 Announce Type: cross Abstract: Clinically actionable, patient-specific hemodynamic assessment, specifically wall shear stress, vortex structure and pressure distributions, is critical for determining risky or unfavorable evolution in Abdominal Aortic Aneurysms (AAA).
By Oscar L. Cruz-Gonzalez, Val\'erie Deplano, Badih Ghattas
arXiv:2608.21677v1 Announce Type: cross
Abstract: Recent mesh-based simulation advances have, in no small part, relied on neural surrogates of two distinct families: global models that route informat...
By Anuj Kumar, Heiko Zimmermann, Josiah Bjorgaard, Jacan Chaplais, Nikolaos Bouklas, Matteo Salvador, Alexander Lavin
The paper introduces the Latent Generative Solver (LGS), a neural PDE solver that combines a Physics VAE, a Pyramidal Flow-Forcing Transformer, and input noising to achieve generalization across twelve PDE families and stable long-term rollouts. LGS matches or surpasses deterministic baselines on one-step predictions, outperforms them on 5- and 10-step rollouts, and significantly reduces long-horizon error while cutting compute costs. It also adapts efficiently to unseen higher-resolution systems, demonstrating strong empirical performance on 2D regular-grid PDE simulations.
By Zituo Chen, Sili Deng