Quality follows upgrading
Tag: cs.NA
arXiv:2603.23658v1 Announce Type: new Abstract: Gradient boosting, a method of building additive ensembles from weak learners, has established itself as a practical and theoretically-motivated approach …
arXiv:2603.23799v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) are increasingly used in mathematical epidemiology to bridge the gap between noisy clinical data and compartmental …
arXiv:2603.20418v1 Announce Type: new Abstract: Unidirectional tapes surface roughness determines the evolution of the degree of intimate contact required for ensuring the thermoplastic molecular diffusion …
arXiv:2603.19463v1 Announce Type: new Abstract: We develop deep learning-based approximation methods for fully nonlinear second-order PDEs on separable Hilbert spaces, such as HJB equations for …
arXiv:2603.17523v1 Announce Type: new Abstract: Neural Operators (NOs) are a powerful deep learning framework designed to learn the solution operator that arise from partial differential …
arXiv:2603.15927v1 Announce Type: new Abstract: We propose a data-driven framework to learn interaction kernels in stochastic multi-agent systems. Our approach aims at identifying the functional …
arXiv:2603.12556v1 Announce Type: new Abstract: We establish empirical scaling laws for Single-Layer Physics-Informed Neural Networks on canonical nonlinear PDEs. We identify a dual optimization failure: …
arXiv:2603.10277v1 Announce Type: new Abstract: In this paper, we propose a fast method for estimating the condition number of sparse matrices using graph neural networks …
arXiv:2603.06752v1 Announce Type: new Abstract: The ensemble Kalman filter (EnKF) is widely used for data assimilation in high-dimensional systems, but its performance often deteriorates for …
arXiv:2603.05696v1 Announce Type: cross Abstract: Ptychography is a computational imaging technique widely used for high-resolution materials characterization, but high-quality reconstructions often require the use of …
arXiv:2603.05727v1 Announce Type: new Abstract: Transformer architectures achieve state-of-the-art performance across a wide range of pattern recognition and natural language processing tasks, but their scaling …
arXiv:2602.12390v1 Announce Type: cross Abstract: We study neural networks with trainable low-degree rational activation functions and show that they are more expressive and parameter-efficient than …
