arXiv:2606. 29723v1 Announce Type: new Abstract: Continuous physical fields represent a large fraction of data under scientific investigation.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
The paper introduces GeoLAMP, a Geometry-aware Latent Autoregressive generative Model designed to solve multiphysics partial differential equations in highly irregular, micro‑scale tortuous geometries. GeoLAMP employs a dual‑encoder graph architecture to capture both global topology and fine‑scale geometry, transforms real‑space fields into compact latent representations, and uses a causal self‑attention transformer with flow matching for stable, scalable block‑wise autoregressive prediction. The model is evaluated on three benchmark datasets—reactive flow, heat convection, and elasticity—showing consistently low errors across the entire rollout horizon.
arXiv:2606. 14999v1 Announce Type: new Abstract: Scientific user facilities generate X-ray scattering data faster than traditional workflows can process them.
arXiv:2608. 05774v1 Announce Type: cross Abstract: Joint-embedding predictive architectures learn by predicting latent representations of missing observations, yet many masked JEPAs are evaluated primarily through the encoders they produce.
Heat Field Signatures (HFS) lift irregular point clouds into a multiscale family of smooth ambient heat fields, enabling closed‑form computation of global and local geometric signatures directly from pairwise distances. HFS captures heat concentration, intrinsic dimension, anisotropy, and scale transitions, and introduces the Heat Dimension Spectrum (HDS) as a compact multiscale summary. The method serves as a descriptor, lightweight learned representation, or feature channel for neural point‑cloud models, outperforming strong baselines on synthetic and real‑world benchmarks while reducing end‑to‑end cost.
arXiv:2603.02843v2 Announce Type: replace Abstract: Generalisation across image scales remains a fundamental challenge for deep networks, which often fail to handle images at scales not seen during t...
arXiv:2608. 16546v1 Announce Type: cross Abstract: Most super-resolution models learn from paired data by supervising only the final high-resolution output.