Zobrazeno 1 - 10
of 127
pro vyhledávání: '"Anitescu, Cosmin"'
Autor:
Eshaghi, Mohammad Sadegh, Bamdad, Mostafa, Anitescu, Cosmin, Wang, Yizheng, Zhuang, Xiaoying, Rabczuk, Timon
This study investigates different Scientific Machine Learning (SciML) approaches for the analysis of functionally graded (FG) porous beams and compares them under a new framework. The beam material properties are assumed to vary as an arbitrary conti
Externí odkaz:
http://arxiv.org/abs/2408.02698
Autor:
Wang, Yizheng, Sun, Jia, Bai, Jinshuai, Anitescu, Cosmin, Eshaghi, Mohammad Sadegh, Zhuang, Xiaoying, Rabczuk, Timon, Liu, Yinghua
AI for partial differential equations (PDEs) has garnered significant attention, particularly with the emergence of Physics-informed neural networks (PINNs). The recent advent of Kolmogorov-Arnold Network (KAN) indicates that there is potential to re
Externí odkaz:
http://arxiv.org/abs/2406.11045
Publikováno v:
Computer Methods in Applied Mechanics and Engineering, Volume 367, 2020, 113066
This paper presents the development of a complete CAD-compatible framework for structural shape optimization in 3D. The boundaries of the domain are described using NURBS while the interior is discretized with B\'ezier tetrahedra. The tetrahedral mes
Externí odkaz:
http://arxiv.org/abs/2312.17575
Flexoelectricity - the generation of electric field in response to a strain gradient - is a universal electromechanical coupling, dominant only at small scales due to its requirement of high strain gradients. This phenomenon is governed by a set of c
Externí odkaz:
http://arxiv.org/abs/2311.15073
Modeling fracture is computationally expensive even in computational simulations of two-dimensional problems. Hence, scaling up the available approaches to be directly applied to large components or systems crucial for real applications become challe
Externí odkaz:
http://arxiv.org/abs/2207.02307
Publikováno v:
In Computers and Structures 1 July 2024 297
The phase-field fracture free-energy functional is non-convex with respect to the displacement and the phase field. This results in a poor performance of the conventional monolithic solvers like the Newton-Raphson method. In order to circumvent this
Externí odkaz:
http://arxiv.org/abs/2111.10649
Autor:
Samaniego, Esteban, Anitescu, Cosmin, Goswami, Somdatta, Nguyen-Thanh, Vien Minh, Guo, Hongwei, Hamdia, Khader, Rabczuk, Timon, Zhuang, Xiaoying
Partial Differential Equations (PDE) are fundamental to model different phenomena in science and engineering mathematically. Solving them is a crucial step towards a precise knowledge of the behaviour of natural and engineered systems. In general, in
Externí odkaz:
http://arxiv.org/abs/1908.10407
We present a new physics informed neural network (PINN) algorithm for solving brittle fracture problems. While most of the PINN algorithms available in the literature minimize the residual of the governing partial differential equation, the proposed
Externí odkaz:
http://arxiv.org/abs/1907.02531
Publikováno v:
In Engineering Analysis with Boundary Elements March 2023 148:243-255