arXiv AI August 10, 2026 By Cooper Simpson, Stephen Becker, Alireza Doostan

In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularization

arXiv:2511. 02659v4 Announce Type: replace-cross Abstract: Focusing on implicit neural representations, we present a novel in situ training protocol that employs limited memory buffers of full and sketched data samples, where the sketched data are leveraged to prevent catastrophic forgetting.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 2

Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation

arXiv:2607. 01164v1 Announce Type: new Abstract: Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying with a reduced memory footprint.

By Landon Dyken, Sharmistha Chakrabarti, Nathan Debardeleben, Steve Petruzza, Qi Wu, Will Usher, Sidharth Kumar

efficiency

arXiv Machine Learning

Jun 10

Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks

arXiv:2606. 10909v1 Announce Type: cross Abstract: Reconstructing local stress fields in heterogeneous microstructures under non-linear, history-dependent loading remains a major computational bottleneck in multi-scale simulations.

By Manuel Ricardo Guevara Garban, Yves Chemisky, \'Etienne Pruli\`ere, Micha\"el Cl\'ement, Martin Abendroth, Bj\"orn Kiefer

arXiv Machine Learning

Jun 10

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms

arXiv:2508. 21022v3 Announce Type: replace Abstract: Subsampled natural gradient descent (SNG) has been used to enable high-precision scientific machine learning, but standard analyses based on stochastic preconditioning fail to provide insight into realistic small-sample settings.

By Gil Goldshlager, Jiang Hu, Lin Lin

arXiv Machine Learning

2d ago

HI-MeshGraphNets: Efficient and Accurate Mesh-based Physics Learning with Hierarchical Multi-scale Graph Neural Networks

arXiv:2608. 13827v1 Announce Type: new Abstract: Machine-learned physical surrogate models have become promising alternatives to mesh-based numerical solvers.

By SiHun Lee, Dong-Hyuk Park, Taesoo Bang, Seung-Hoon Kang

benchmarks

arXiv AI

Jul 2

Improving Sparse-View 3DGS Generalization via Flat Minima Optimization

arXiv:2607. 00885v1 Announce Type: cross Abstract: Recent advances in neural rendering have established 3D Gaussian Splatting (3DGS) as a highly efficient representation for novel view synthesis, enabling fast training and real-time rendering with strong fidelity.

By Kangmin Seo, Sangeek Hyun, MinKyu Lee, Jae-Pil Heo

arXiv AI

Jun 18

Grids Often Outperform Implicit Neural Representations at Compressing Dense Signals

arXiv:2506. 11139v3 Announce Type: replace-cross Abstract: Implicit Neural Representations (INRs) have recently shown impressive results, but their fundamental capacity, implicit biases, and scaling behavior remain poorly understood.

By Namhoon Kim, Sara Fridovich-Keil

Related stories

Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation

Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms

HI-MeshGraphNets: Efficient and Accurate Mesh-based Physics Learning with Hierarchical Multi-scale Graph Neural Networks

Improving Sparse-View 3DGS Generalization via Flat Minima Optimization

Grids Often Outperform Implicit Neural Representations at Compressing Dense Signals