Results 201 to 210 of about 3,581,525 (255)

Training a deep operator network as a surrogate solver for two-dimensional parabolic-equation models.

Journal of the Acoustical Society of America, 2023
Parabolic equations (PEs) are useful for modeling sound propagation in a range-dependent environment. However, this approach entails approximating a leading-order cross-derivative term in the PE square-root operators.
Liang Xu, Haigang Zhang, Minghui Zhang
semanticscholar   +1 more source

On Landau-Type Approximation Operators

Mediterranean Journal of Mathematics, 2021
After presenting a history of Landau's operators, the authors propose a new generalization of them. A family of convolution type operators depending on a real parameter and acting on functions defined on the whole real axis is constructed. A remarkable property of these new operators is that they reproduce the affine functions, a feature less commonly ...
Octavian Agratini, Sorin G. Gal
openaire   +2 more sources

Approximate Inverses of Operators

Circuits, Systems, and Signal Processing, 2003
Let \(H,K\) be normed spaces with the same system of scalars. The invertibility of linear (respectively nonlinear) operators \(N:H \to K\) is characterized in terms of their approximate inverses and their standard (Lipschitz) operator norm.
openaire   +1 more source

Design and Analysis of Approximate Compressors for Balanced Error Accumulation in MAC Operator

IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2021
In this paper, we present a novel approximate computing scheme suitable for realizing the energy-efficient multiply-accumulate (MAC) processing. In contrast to the prior works that suffer from the error accumulation limiting the approximate range, we ...
Gunho Park, J. Kung, Youngjoo Lee
semanticscholar   +1 more source

Characterization of approximate monotone operators

2022
The authors study approximate monotone operators. They show that a well-known property of monotone operators, namely, representing by convex functions, remains valid for this larger class of operators. In this general framework, results of \textit{S.
Rezaei, Mahboubeh, Mirsaney, Zahra Sadat
openaire   +2 more sources

Time operators, innovations and approximations

Chaos, Solitons & Fractals, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Suchanecki, Zdzisław, Antoniou, Ioannis
openaire   +2 more sources

Operator Learning: Algorithms and Analysis

arXiv.org
Operator learning refers to the application of ideas from machine learning to approximate (typically nonlinear) operators mapping between Banach spaces of functions.
Nikola B. Kovachki, S. Lanthaler, Andrew M. Stuart   +2 more
semanticscholar   +1 more source

MODNO: Multi Operator Learning With Distributed Neural Operators

Computer Methods in Applied Mechanics and Engineering
The study of operator learning involves the utilization of neural networks to approximate operators. Traditionally, the focus has been on single-operator learning (SOL).
Zecheng Zhang
semanticscholar   +1 more source

Approximation operators and tauberian constants

Israel Journal of Mathematics, 1969
The explicit expression of the smallest constantC satisfying $$\mathop {lim}\limits_{\lambda \to \infty } \left| {t_{n(\lambda )}^{(1)} - t_{m(\lambda )}^{(2)} } \right| \leqq C.
Jakimovski, Amnon, Livne, A.
openaire   +1 more source

Approximation by Gamma type operators

Mathematical Methods in the Applied Sciences, 2020
In this study, we introduce newly defined Gamma operators which preserve constants and e2μ·, μ>0 functions. In accordance with this purpose, we focus on their approximation properties such as uniform convergence, rate of convergence, asymptotic formula, and saturation results.
Serife Nur Deveci, Tuncer Acar, Osman Alagoz   +2 more
openaire   +3 more sources