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of 4 677
pro vyhledávání: '"A., Porwal"'
Autor:
Ahmad, Ghazi Shazan, Agarwal, Shubham, Mitra, Subrata, Rossi, Ryan, Doshi, Manav, Porwal, Vibhor, Paila, Syam Manoj Kumar
Automated visualization recommendations (vis-rec) help users to derive crucial insights from new datasets. Typically, such automated vis-rec models first calculate a large number of statistics from the datasets and then use machine-learning models to
Externí odkaz:
http://arxiv.org/abs/2411.18657
Autor:
Porwal, Anupreet, Rodriguez, Abel
This paper introduces Dirichlet process mixtures of block $g$ priors for model selection and prediction in linear models. These priors are extensions of traditional mixtures of $g$ priors that allow for differential shrinkage for various (data-select
Externí odkaz:
http://arxiv.org/abs/2411.00471
This paper will illustrate the usage of Machine Learning algorithms on US College Scorecard datasets. For this paper, we will use our knowledge, research, and development of a predictive model to compare the results of all the models and predict the
Externí odkaz:
http://arxiv.org/abs/2406.08071
Autor:
Porwal, Kamana, Singla, Ritesh
In this article, a posteriori error analysis of the elliptic obstacle problem is addressed using hybrid high-order methods. The method involve cell unknowns represented by degree-$r$ polynomials and face unknowns represented by degree-$s$ polynomials
Externí odkaz:
http://arxiv.org/abs/2405.04961
Data generated by edge devices has the potential to train intelligent autonomous systems across various domains. Despite the emergence of diverse machine learning approaches addressing privacy concerns and utilizing distributed data, security issues
Externí odkaz:
http://arxiv.org/abs/2403.06797
We present efficient MATLAB implementations of the lowest-order primal hybrid finite element method (FEM) for linear second-order elliptic and parabolic problems with mixed boundary conditions in two spatial dimensions. We employ the Crank-Nicolson f
Externí odkaz:
http://arxiv.org/abs/2401.15557
In this paper, we develop a new residual-based pointwise a posteriori error estimator of the quadratic finite element method for the Signorini problem. The supremum norm a posteriori error estimates enable us to locate the singularities locally to co
Externí odkaz:
http://arxiv.org/abs/2401.02181
An a posteriori error bound for the pointwise error of the quadratic discontinuous Galerkin method for the unilateral contact problem on polygonal domain is presented. The pointwise a posteriori error analysis is based on the direct use of a priori e
Externí odkaz:
http://arxiv.org/abs/2401.02176
Autor:
Porwal, Kamana, Wadhawan, Tanvi
In this article, we addressed the numerical solution of a non-linear evolutionary variational inequality, which is encountered in the investigation of quasi-static contact problems. Our study encompasses both the semi-discrete and fully-discrete sche
Externí odkaz:
http://arxiv.org/abs/2401.02170
Autor:
Porwal, Kamana, Wadhawan, Tanvi
In this article, we employ discontinuous Galerkin (DG) methods for the finite element approximation of the frictionless unilateral contact problem using quadratic finite elements over simplicial triangulation. We first establish an optimal \textit{a
Externí odkaz:
http://arxiv.org/abs/2401.02120