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Deep learning method is of great importance in solving partial differential equations. In this paper, inspired by the failure-informed idea proposed by Gao et.al. (SIAM Journal on Scientific Computing 45(4)(2023)) and as an improvement, a…

Numerical Analysis · Mathematics 2024-04-30 Jingyong Ying , Yaqi Xie , Jiao Li , Hongqiao Wang

Finite difference method was extended to unstructured meshes to solve Euler equations. The spatial discretization is made of two steps. First, numerical fluxes are computed at the middle point of each edge with high order accuracy. In this…

Computational Physics · Physics 2021-02-26 Meiyuan Zhen , Kun Qu , Jinsheng Cai

We extend the recently introduced divergence-conforming immersed boundary (DCIB) method [1] to fluid-structure interaction (FSI) problems involving closed co-dimension one solids. We focus on capsules and vesicles, whose discretization is…

We propose a numerical method for solving block-structured mesh partitioning problems based on the variational level-set method of (Zhao et al., J Comput Phys 127, 1996) which has been widely used in many partitioning problems such as image…

Computational Physics · Physics 2018-01-12 Shucheng Pan , Xiangyu Hu , Nikolaus. A. Adams

The vector field of a mixed-monotone system is decomposable via a decomposition function into increasing (cooperative) and decreasing (competitive) components, and this decomposition allows for, e.g., efficient computation of reachable sets…

Systems and Control · Electrical Eng. & Systems 2020-05-25 Matthew Abate , Maxence Dutreix , Samuel Coogan

The problem of extrapolation and interpolation of asymptotic series is considered. Several new variants of improving the accuracy of the self-similar approximants are suggested. The methods are illustrated by examples typical of chemical…

Mathematical Physics · Physics 2010-04-08 V. I. Yukalov , E. P. Yukalova , S. Gluzman

In the feature maps of CNNs, there commonly exists considerable spatial redundancy that leads to much repetitive processing. Towards reducing this superfluous computation, we propose to compute features only at sparsely sampled locations,…

Computer Vision and Pattern Recognition · Computer Science 2020-09-07 Zhenda Xie , Zheng Zhang , Xizhou Zhu , Gao Huang , Stephen Lin

Mechanistic interpretability often uses activation patching, causal tracing, path patching, and steering directions to reveal behaviorally meaningful directions in Transformer activation space. This paper develops a field-theoretic…

Machine Learning · Computer Science 2026-05-26 David N. Olivieri , Antonio F. Pérez Rodríguez

In this short note we describe a simple adaptation of biharmonic surfaces to interpolate boundary cross-derivatives given in ribbon form, and compare with the recently proposed Generalized B-spline patches.

Numerical Analysis · Mathematics 2023-11-23 Márton Vaitkus

In this paper, we present a contraction-guided adaptive partitioning algorithm for improving interval-valued robust reachable set estimates in a nonlinear feedback loop with a neural network controller and disturbances. Based on an estimate…

Systems and Control · Electrical Eng. & Systems 2024-01-23 Akash Harapanahalli , Saber Jafarpour , Samuel Coogan

We present an extension of the PatchworkMHD code [1], itself an MHD-capable extension of the Patchwork code [2], for which several algorithms presented here were co-developed. Its purpose is to create a multipatch scheme compatible with…

The simulation of viscoelastic time-evolution problems described by a large number of internal variables and with a large spectrum of relaxation times requires high computational resources for their resolution. Furthermore, the internal…

Computational Engineering, Finance, and Science · Computer Science 2024-08-12 Sebastian Rodriguez , Angelo Pasquale , Jad Mounayer , Diego Canales , Marianne Beringhier , Chady Ghnatios , Amine Ammar , Francisco Chinesta

Adaptive gradient methods are typically used for training over-parameterized models. To better understand their behaviour, we study a simplistic setting -- smooth, convex losses with models over-parameterized enough to interpolate the data.…

Machine Learning · Computer Science 2021-02-22 Sharan Vaswani , Issam Laradji , Frederik Kunstner , Si Yi Meng , Mark Schmidt , Simon Lacoste-Julien

Convex optimizers have known many applications as differentiable layers within deep neural architectures. One application of these convex layers is to project points into a convex set. However, both forward and backward passes of these…

Machine Learning · Computer Science 2020-11-16 Riad Akrour , Asma Atamna , Jan Peters

In this paper, using a pseudospectral approach, we develop operational matrices based on the shifted Chebyshev polynomials to approximate numerically Caputo fractional derivatives and Riemann-Liouville fractional integrals. In order to make…

Numerical Analysis · Mathematics 2025-11-17 Francisco de la Hoz , Peru Muniain

The present paper concerns filtered de la Vall\'ee Poussin (VP) interpolation at the Chebyshev nodes of the four kinds. This approximation model is interesting for applications because it combines the advantages of the classical Lagrange…

Numerical Analysis · Mathematics 2021-01-13 D. Occorsio , W. Themistoclakis

Interpolation methodologies have been widely used within the domain of indoor positioning systems. However, existing indoor positioning interpolation algorithms exhibit several inherent limitations, including reliance on complex…

Machine Learning · Computer Science 2023-11-28 Suorong Yang , Geng Zhang , Jian Zhao , Furao Shen

In industrial defect segmentation tasks, while pixel accuracy and Intersection over Union (IoU) are commonly employed metrics to assess segmentation performance, the output consistency (also referred to equivalence) of the model is often…

Computer Vision and Pattern Recognition · Computer Science 2023-10-02 Zhen Qu , Xian Tao , Fei Shen , Zhengtao Zhang , Tao Li

This article performs a unified convergence analysis of a variety of numerical methods for a model of the miscible displacement of one incompressible fluid by another through a porous medium. The unified analysis is enabled through the…

Numerical Analysis · Mathematics 2018-03-07 Jérôme Droniou , Robert Eymard , Alain Prignet , Kyle S. Talbot

Compression is a crucial solution for data reduction in modern scientific applications due to the exponential growth of data from simulations, experiments, and observations. Compression with progressive retrieval capability allows users to…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-04-08 Zhuoxun Yang , Sheng Di , Longtao Zhang , Ruoyu Li , Ximiao Li , Jiajun Huang , Jinyang Liu , Franck Cappello , Kai Zhao