arXiv:2606. 31574v1 Announce Type: cross Abstract: Accurate modeling of the divertor temperature field is essential for preventing material melting and damage and for extending the service life of fusion devices.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2510. 15968v2 Announce Type: replace-cross Abstract: Thermal management in 3D ICs is increasingly challenging due to higher power densities.
arXiv:2607. 24513v1 Announce Type: new Abstract: Transformer architectures have attracted increasing attention for solving partial differential equations (PDEs), owing to their flexibility in handling irregular discretizations and their ability to capture long-range physical dependencies.
arXiv:2602. 02788v2 Announce Type: replace-cross Abstract: We aim to develop physics foundation models for science and engineering that provide real-time solutions to Partial Differential Equations (PDEs) which preserve structure and accuracy under adaptation to unseen geometries.
arXiv:2602. 11626v3 Announce Type: replace-cross Abstract: Learning solution operators on arbitrary geometries remains a central challenge in scientific machine learning, especially for many-query simulation, physics-informed learning, and evolving geometries requiring accurate, geometry-aware predictions at arbitrary spatial locations.
arXiv:2608. 13260v1 Announce Type: new Abstract: Accurate modeling and forecasting of power transformer thermal behavior are critical for reliability, asset lifetime, and optimized power system operation.
arXiv:2607. 20321v1 Announce Type: cross Abstract: Neural surrogates are widely used in scientific machine learning for fast prediction of three-dimensional (3D) thermo-fluid fields.