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Trustworthy machine learning necessitates meticulous regulation of model reliance on non-robust features. We propose a framework to delineate and regulate such features by attributing model predictions to the input. Within our approach,…

Machine Learning · Computer Science 2024-07-10 Peiyu Yang , Naveed Akhtar , Mubarak Shah , Ajmal Mian

As two-dimensional fluid shells, lipid bilayer membranes resist bending and stretching but are unable to sustain shear stresses. This property gives membranes the ability to adopt dramatic shape changes. In this paper, a finite element…

Biological Physics · Physics 2009-11-13 Lin Ma , William S. Klug

Overconfidence has been shown to impair generalization and calibration of a neural network. Previous studies remedy this issue by adding a regularization term to a loss function, preventing a model from making a peaked distribution. Label…

Machine Learning · Computer Science 2022-10-26 Dongkyu Lee , Ka Chun Cheung , Nevin L. Zhang

In this contribution we present a survey of concepts in localized model order reduction methods for parameterized partial differential equations. The key concept of localized model order reduction is to construct local reduced spaces that…

Numerical Analysis · Mathematics 2019-10-29 Andreas Buhr , Laura Iapichino , Mario Ohlberger , Stephan Rave , Felix Schindler , Kathrin Smetana

This article considers a model problem of elastoplasticity with linearly kinematic hardening and presents hp-finite element discretizations of two equivalent weak formulations each having their respective advantages. A mixed variational…

Numerical Analysis · Mathematics 2026-05-12 Patrick Bammer , Lothar Banz , Miriam Schönauer , Andreas Schröder

We present a cut finite element method for shape optimization in the case of linear elasticity. The elastic domain is defined by a level-set function, and the evolution of the domain is obtained by moving the level-set along a velocity…

Numerical Analysis · Mathematics 2019-02-05 Erik Burman , Daniel Elfverson , Peter Hansbo , Mats G. Larson , Karl Larsson

To minimize or upper-bound the value of a function "robustly", we might instead minimize or upper-bound the "epsilon-robust regularization", defined as the map from a point to the maximum value of the function within an epsilon-radius. This…

Optimization and Control · Mathematics 2010-06-10 Adrian S. Lewis , C. H. Jeffrey Pang

Strain localization is responsible for mesh dependence in numerical analyses concerning a vast variety of fields such as solid mechanics, dynamics, biomechanics and geomechanics. Therefore, numerical methods that regularize strain…

Applied Physics · Physics 2021-11-03 Alexandros Stathas , Ioannis Stefanou

We initiate a program of average smoothness analysis for efficiently learning real-valued functions on metric spaces. Rather than using the Lipschitz constant as the regularizer, we define a local slope at each point and gauge the function…

Statistics Theory · Mathematics 2020-11-10 Yair Ashlagi , Lee-Ad Gottlieb , Aryeh Kontorovich

We construct a finite element approximation of a strain-limiting elastic model on a bounded open domain in $\mathbb{R}^d$, $d \in \{2,3\}$. The sequence of finite element approximations is shown to exhibit strong convergence to the unique…

Numerical Analysis · Mathematics 2020-04-02 Andrea Bonito , Vivette Girault , Endre Süli

We formulate a cut finite element method for linear elasticity based on higher order elements on a fixed background mesh. Key to the method is a stabilization term which provides control of the jumps in the derivatives of the finite element…

Numerical Analysis · Mathematics 2019-02-05 Peter Hansbo , Mats G. Larson , Karl Larsson

The first part of the cumulative thesis contains the numerical analysis of different $hp$-finite element discretizations related to two different weak formulations of a model problem in elastoplasticity with linearly kinematic hardening.…

Numerical Analysis · Mathematics 2024-02-06 Patrick Bammer

Feature attributions are a popular tool for explaining the behavior of Deep Neural Networks (DNNs), but have recently been shown to be vulnerable to attacks that produce divergent explanations for nearby inputs. This lack of robustness is…

Machine Learning · Computer Science 2020-10-23 Zifan Wang , Haofan Wang , Shakul Ramkumar , Matt Fredrikson , Piotr Mardziel , Anupam Datta

When homogenizing elliptic partial differential equations, the so-called corrector problem is pivotal to compute the macroscale effective coefficients from the microscale information. To solve this corrector problem in the periodic setting,…

Numerical Analysis · Mathematics 2014-11-04 Sebastien Brisard , Frederic Legoll

Lipschitz-based certification offers efficient, deterministic robustness guarantees but has struggled to scale in model size, training efficiency, and ImageNet performance. We introduce \emph{LipNeXt}, the first \emph{constraint-free} and…

Machine Learning · Computer Science 2026-01-27 Kai Hu , Haoqi Hu , Matt Fredrikson

A two dimensional amorphous material is modeled as an assembly of mesoscopic elemental pieces coupled together to form an elastically coherent structure. Plasticity is introduced as the existence of different minima in the energy landscape…

Materials Science · Physics 2007-07-05 E. A. Jagla

We establish interior Lipschitz estimates at the macroscopic scale for solutions to systems of linear elasticity with rapidly oscillating periodic coefficients and mixed boundary conditions in domains periodically perforated at a…

Analysis of PDEs · Mathematics 2017-04-12 B. Chase Russell

Trimmed (multi-patch) geometries are the state-of-the-art technology in computer-aided design for industrial applications such as automobile crashworthiness. In this context, fast solution techniques extensively rely on explicit time…

Numerical Analysis · Mathematics 2025-02-04 Yannis Voet , Espen Sande , Annalisa Buffa

A new approach is developed to derive an analytical form for mobility corrections in phase-field models for pure material solidification. Similar to the thin interface limit approach (Karma and Rappel, 1996) it seeks to remove systematic…

Computational Physics · Physics 2019-05-09 Stephan Hubig , Raphael Schiedung , Ingo Steinbach

We study a generalization of the manifold-valued Rudin-Osher-Fatemi (ROF) model, which involves an initial datum $f$ mapping from a curved compact surface with smooth boundary to a complete, connected and smooth $n$-dimensional Riemannian…

Analysis of PDEs · Mathematics 2026-03-31 Esther Cabezas-Rivas , Salvador Moll , Vicent Pallardó-Julià