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With the development of new remote sensing technology, large or even massive spatial datasets covering the globe become available. Statistical analysis of such data is challenging. This article proposes a semiparametric approach to model…

Methodology · Statistics 2019-10-24 Pulong Ma , Emily L. Kang

FeFETs hold strong potential for advancing memory and logic technologies, but their inherent randomness arising from both operational cycling and fabrication variability poses significant challenges for accurate and reliable modeling.…

Machine Learning · Computer Science 2025-08-06 Tasnia Nobi Afee , Jack Hutchins , Md Mazharul Islam , Thomas Kampfe , Ahmedullah Aziz

Understanding and modeling the constitutive behavior of concrete is crucial for civil and defense applications, yet widely used phenomenological models such as Karagozian \& Case concrete (KCC) model depend on empirically calibrated failure…

Machine Learning · Computer Science 2026-01-08 Chenyang Li , Himanshu Sharma , Youcai Wu , Joseph Magallanes , K. T. Ramesh , Michael D. Shields

Robots must be able to understand their surroundings to perform complex tasks in challenging environments and many of these complex tasks require estimates of physical properties such as friction or weight. Estimating such properties using…

Robotics · Computer Science 2024-05-30 Parker Ewen , Hao Chen , Yuzhen Chen , Anran Li , Anup Bagali , Gitesh Gunjal , Ram Vasudevan

We introduce a coupled system of PDEs for the modeling of the fluid-fluid and fluid-solid interaction in a poroelastic material with a single static fracture. The fluid flow in the fracture is modeled by a lower-dimensional Darcy equation,…

Numerical Analysis · Mathematics 2016-08-15 Katja K. Hanowski , Oliver Sander

The function of biomolecules such as proteins depends on their ability to interconvert between a wide range of structures or "conformations." Researchers have endeavored for decades to develop computational methods to predict the…

Biomolecules · Quantitative Biology 2026-02-05 Daniel D. Richman , Jessica Karaguesian , Carl-Mikael Suomivuori , Ron O. Dror

Linear systems occur throughout engineering and the sciences, most notably as differential equations. In many cases the forcing function for the system is unknown, and interest lies in using noisy observations of the system to infer the…

We tackle the challenge of feature embedding for the purposes of improving the click-through rate prediction process. We select three models: logistic regression, factorization machines and deep factorization machines, as our baselines and…

Machine Learning · Computer Science 2022-09-21 Samo Pahor , Davorin Kopič , Jure Demšar

Optimization of the intermetallic layer thickness and the suppression of interfacial defects are key elements to improve the load bearing capacity of dissimilar joints. However, till date we do not have a systematic tool to investigate the…

Existing high-dimensional statistical methods are largely established for analyzing individual-level data. In this work, we study estimation and inference for high-dimensional linear models where we only observe "proxy data", which include…

Methodology · Statistics 2022-01-12 Sai Li , T. Tony Cai , Hongzhe Li

The mainstream researche in deep metric learning can be divided into two genres: proxy-based and pair-based methods. Proxy-based methods have attracted extensive attention due to the lower training complexity and fast network convergence.…

Information Retrieval · Computer Science 2023-04-19 Xinyue Li , Jian Wang , Wei Song , Yanling Du , Zhixiang Liu

Physics-based simulation is essential for developing and evaluating robot manipulation policies, particularly in scenarios involving deformable objects and complex contact interactions. However, existing simulators often struggle to balance…

We present a complete numerical analysis for a general discretization of a coupled flow-mechanics model in fractured porous media, considering single-phase flows and including frictionless contact at matrix-fracture interfaces, as well as…

Numerical Analysis · Mathematics 2024-06-14 Francesco Bonaldi , Jérôme Droniou , Roland Masson

Epoxy is widely used primarily as a matrix material in the manufacture of polymer matrix composite. Epoxy behavior under compression load has to be understood before the mechanical behavior of polymer matrix composite can be accurately…

Materials Science · Physics 2014-02-28 Irfan Dwi Aditya , Widayani , Sparisoma Viridi , Siti Nurul Khotimah

This Letter introduces an approach for precisely designing surface friction properties using a conditional generative machine learning model, specifically a diffusion denoising probabilistic model (DDPM). We created a dataset of synthetic…

Computational Physics · Physics 2024-01-11 Even Marius Nordhagen , Henrik Andersen Sveinsson , Anders Malthe-Sørenssen

Understanding material failure is critical for designing stronger and lighter structures by identifying weaknesses that could be mitigated. Existing full-physics numerical simulation techniques involve trade-offs between speed, accuracy,…

In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks, which results in a significant speedup over traditional…

Machine Learning · Computer Science 2013-09-02 Tamir Hazan , Alexander Schwing , David McAllester , Raquel Urtasun

Long-span bridges are subjected to a multitude of dynamic excitations during their lifespan. To account for their effects on the structural system, several load models are used during design to simulate the conditions the structure is…

Machine Learning · Computer Science 2023-08-21 Gledson Rodrigo Tondo , Igor Kavrakov , Guido Morgenthal

In the present work, a rate-dependent cohesive zone model for the fracture of polymeric interfaces is presented. Inverse calibration of parameters for such complex models through trial and error is computationally tedious due to the large…

Computational Engineering, Finance, and Science · Computer Science 2023-11-15 Ponkrshnan Thiagarajan , Trisha Sain , Susanta Ghosh

We present a multi-scale computational framework suitable for designing solid lubricant interfaces fully in silico. The approach is based on stochastic thermodynamics founded on the classical thermally activated two-dimensional…

Materials Science · Physics 2022-04-27 Paola C. Torche , Andrea Silva , Denis Kramer , Tomas Polcar , Ondrej Hovorka