Zobrazeno 1 - 10
of 4 769
pro vyhledávání: '"P. Apel"'
With the recent advances in AI programming assistants such as GitHub Copilot, programming is not limited to classical programming languages anymore--programming tasks can also be expressed and solved by end-users in natural text. Despite the availabi
Externí odkaz:
http://arxiv.org/abs/2412.12471
Autor:
Bergum, Annabelle, Maurer, Anna-Maria, Peitek, Norman, Bader, Regine, Mecklinger, Axel, Demberg, Vera, Siegmund, Janet, Apel, Sven
As software pervades more and more areas of our professional and personal lives, there is an ever-increasing need to maintain software, and for programmers to be able to efficiently write and understand program code. In the first study of its kind, w
Externí odkaz:
http://arxiv.org/abs/2412.10099
Autor:
Rezaei, Shahed, Asl, Reza Najian, Taghikhani, Kianoosh, Moeineddin, Ahmad, Kaliske, Michael, Apel, Markus
We introduce a method that combines neural operators, physics-informed machine learning, and standard numerical methods for solving PDEs. The proposed approach extends each of the aforementioned methods and unifies them within a single framework. We
Externí odkaz:
http://arxiv.org/abs/2407.04157
Modeling structure and behavior of software systems plays a crucial role in the industrial practice of software engineering. As with other software engineering artifacts, software models are subject to evolution. Supporting modelers in evolving softw
Externí odkaz:
http://arxiv.org/abs/2406.17651
Autor:
Yamazaki, Yusuke, Harandi, Ali, Muramatsu, Mayu, Viardin, Alexandre, Apel, Markus, Brepols, Tim, Reese, Stefanie, Rezaei, Shahed
Publikováno v:
Eng. Comput., 1-29 (2024)
We propose a novel finite element-based physics-informed operator learning framework that allows for predicting spatiotemporal dynamics governed by partial differential equations (PDEs). The proposed framework employs a loss function inspired by the
Externí odkaz:
http://arxiv.org/abs/2405.12465
Autor:
Rezaei, Shahed, Asl, Reza Najian, Faroughi, Shirko, Asgharzadeh, Mahdi, Harandi, Ali, Koopas, Rasoul Najafi, Laschet, Gottfried, Reese, Stefanie, Apel, Markus
To obtain fast solutions for governing physical equations in solid mechanics, we introduce a method that integrates the core ideas of the finite element method with physics-informed neural networks and concept of neural operators. This approach gener
Externí odkaz:
http://arxiv.org/abs/2404.00074
The article examines a linear-quadratic Neumann control problem that is governed by a non-coercive elliptic equation. Due to the non-self-adjoint nature of the linear control-to-state operator, it is necessary to independently study both the state an
Externí odkaz:
http://arxiv.org/abs/2403.12551
Autor:
Apel, Thomas, Zilk, Philipp
The Laplace eigenvalue problem on circular sectors has eigenfunctions with corner singularities. Standard methods may produce suboptimal approximation results. To address this issue, a novel numerical algorithm that enhances standard isogeometric ana
Externí odkaz:
http://arxiv.org/abs/2402.16589
Autor:
Wyrich, Marvin, Apel, Sven
Valid empirical studies build confidence in scientific findings. Fortunately, it is now common for software engineering researchers to consider threats to validity when designing their studies and to discuss them as part of their publication. Yet, in
Externí odkaz:
http://arxiv.org/abs/2402.08608
Lipolysis is a life-essential metabolic process, which supplies fatty acids stored in lipid droplets to the body in order to match the demands of building new cells and providing cellular energy. In this paper, we present a first mathematical modelli
Externí odkaz:
http://arxiv.org/abs/2401.17935