Zobrazeno 1 - 10
of 1 402
pro vyhledávání: '"Lazarov P"'
We introduce a novel method for solving density-based topology optimization problems: \underline{Si}gmoidal \underline{M}irror descent with a \underline{P}rojected \underline{L}atent variable (SiMPL). The SiMPL method (pronounced as "the simple metho
Externí odkaz:
http://arxiv.org/abs/2411.19421
Autor:
Kepaptsoglou, Demie, Castellanos-Reyes, José Ángel, Kerrigan, Adam, Nascimento, Júlio Alves Do, Zeiger, Paul M., Hajraoui, Khalil El, Idrobo, Juan Carlos, Mendis, Budhika G., Bergman, Anders, Lazarov, Vlado K., Rusz, Ján, Ramasse, Quentin M.
The miniaturisation of transistors is approaching its limits due to challenges in heat management and information transfer speed. To overcome these obstacles, emerging technologies such as spintronics are being developed, which leverage the electron'
Externí odkaz:
http://arxiv.org/abs/2410.02908
We present a rigorous convergence analysis of a new method for density-based topology optimization: Sigmoidal Mirror descent with a Projected Latent variable. SiMPL provides point-wise bound preserving design updates and faster convergence than other
Externí odkaz:
http://arxiv.org/abs/2409.19341
This work highlights an approach for incorporating realistic uncertainties into scientific computing workflows based on finite elements, focusing on applications in computational mechanics and design optimization. We leverage Mat\'ern-type Gaussian r
Externí odkaz:
http://arxiv.org/abs/2403.03658
Autor:
Andrej, Julian, Atallah, Nabil, Bäcker, Jan-Phillip, Camier, John, Copeland, Dylan, Dobrev, Veselin, Dudouit, Yohann, Duswald, Tobias, Keith, Brendan, Kim, Dohyun, Kolev, Tzanio, Lazarov, Boyan, Mittal, Ketan, Pazner, Will, Petrides, Socratis, Shiraiwa, Syun'ichi, Stowell, Mark, Tomov, Vladimir
The MFEM (Modular Finite Element Methods) library is a high-performance C++ library for finite element discretizations. MFEM supports numerous types of finite element methods and is the discretization engine powering many computational physics and en
Externí odkaz:
http://arxiv.org/abs/2402.15940
Autor:
Nascimento, Julio A. do, Hasnip, Phil J., Cavill, S. A., Cossu, Fabrizio, Kepaptsoglou, Demie, Ramasse, Quentin M., Kerrigan, Adam, Lazarov, Vlado K.
We explore the inelastic spectra of electrons impinging in a magnetic system. The methodology here presented is intended to highlight the charge-dependent interaction of the electron beam in a STEM-EELS experiment, and the local vector potential gene
Externí odkaz:
http://arxiv.org/abs/2401.12302
Autor:
Nascimento, Julio A. do, Kerrigan, Adam, Cavill, S. A., Hasnip, Phil J., Kepaptsoglou, Demie, Ramasse, Quentin M., Lazarov, Vlado K.
We present a methodology based on the calculation of the inelastic scattering from magnons via the spin scattering function in confined geometries such as thin films using a second quantization formalism, for both ferromagnetic and antiferromagnetic
Externí odkaz:
http://arxiv.org/abs/2312.07715
Autor:
Buschmeier, Hendrik, Buhl, Heike M., Kern, Friederike, Grimminger, Angela, Beierling, Helen, Fisher, Josephine, Groß, André, Horwath, Ilona, Klowait, Nils, Lazarov, Stefan, Lenke, Michael, Lohmer, Vivien, Rohlfing, Katharina, Scharlau, Ingrid, Singh, Amit, Terfloth, Lutz, Vollmer, Anna-Lisa, Wang, Yu, Wilmes, Annedore, Wrede, Britta
Explainability has become an important topic in computer science and artificial intelligence, leading to a subfield called Explainable Artificial Intelligence (XAI). The goal of providing or seeking explanations is to achieve (better) 'understanding'
Externí odkaz:
http://arxiv.org/abs/2311.08760
Autor:
Cossu, Fabrizio, Nascimento, Jùlio Alves Do, Cavill, Stuart A., Di Marco, Igor, Lazarov, Vlado K., Kim, Heung-Sik
Publikováno v:
Phys. Rev. B 109, 045435 (2024)
Using first-principles techniques, we study the structural, magnetic and electronic properties of (111)-oriented (LaMnO$_3$)$_{2n}$$\vert$(SrMnO$_3$)$_{n}$ superlattices of varying thickness ($n=2,4,6$). We find that the properties of the thinnest su
Externí odkaz:
http://arxiv.org/abs/2311.01081
Autor:
Bollapragada, Raghu, Karamanli, Cem, Keith, Brendan, Lazarov, Boyan, Petrides, Socratis, Wang, Jingyi
The primary goal of this paper is to provide an efficient solution algorithm based on the augmented Lagrangian framework for optimization problems with a stochastic objective function and deterministic constraints. Our main contribution is combining
Externí odkaz:
http://arxiv.org/abs/2305.01018