Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Klein, Dominik K."'
We present neural network-based constitutive models for hyperelastic geometrically exact beams. The proposed models are physics-augmented, i.e., formulated to fulfill important mechanical conditions by construction. Strains and curvatures of the beam
Externí odkaz:
http://arxiv.org/abs/2407.00640
In the present work, the applicability of physics-augmented neural network (PANN) constitutive models for complex electro-elastic finite element analysis is demonstrated. For the investigations, PANN models for electro-elastic material behavior at fi
Externí odkaz:
http://arxiv.org/abs/2402.07007
In the present work, neural networks are applied to formulate parametrised hyperelastic constitutive models. The models fulfill all common mechanical conditions of hyperelasticity by construction. In particular, partially input-convex neural network
Externí odkaz:
http://arxiv.org/abs/2307.03463
In the present work, advanced spatial and temporal discretization techniques are tailored to hyperelastic physics-augmented neural networks, i.e., neural network based constitutive models which fulfill all relevant mechanical conditions of hyperelast
Externí odkaz:
http://arxiv.org/abs/2306.09866
Autor:
Linden, Lennart, Klein, Dominik K., Kalina, Karl A., Brummund, Jörg, Weeger, Oliver, Kästner, Markus
Publikováno v:
Journal of the Mechanics and Physics of Solids (2023)
In the present work, a hyperelastic constitutive model based on neural networks is proposed which fulfills all common constitutive conditions by construction, and in particular, is applicable to compressible material behavior. Using different sets of
Externí odkaz:
http://arxiv.org/abs/2302.02403
In the present work, a machine learning based constitutive model for electro-mechanically coupled material behavior at finite deformations is proposed. Using different sets of invariants as inputs, an internal energy density is formulated as a convex
Externí odkaz:
http://arxiv.org/abs/2206.05139
In the present work, two machine learning based constitutive models for finite deformations are proposed. Using input convex neural networks, the models are hyperelastic, anisotropic and fulfill the polyconvexity condition, which implies ellipticity
Externí odkaz:
http://arxiv.org/abs/2106.14623
Publikováno v:
In Journal of the Mechanics and Physics of Solids February 2022 159
