Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Fuhg, Jan N."'
Constitutive modeling lies at the core of mechanics, allowing us to map strains onto stresses for a material in a given mechanical setting. Historically, researchers relied on phenomenological modeling where simple mathematical relationships were der
Externí odkaz:
http://arxiv.org/abs/2408.14615
Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonlinear data and their ability to incorporate constitutive constraints,
Externí odkaz:
http://arxiv.org/abs/2404.15562
Autor:
van Wees, Lloyd, Shankar, Karthik, Fuhg, Jan N., Bouklas, Nikolaos, Shade, Paul, Obstalecki, Mark, Kasemer, Matthew
In this study, we present a methodology to predict the macroscopic yield surface of metals and metallic alloys with general crystallographic textures. In previous work, we have established the use of partially input convex neural networks (pICNN) as
Externí odkaz:
http://arxiv.org/abs/2404.03863
Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints and to overcome the challenging and time-consuming task of formulatin
Externí odkaz:
http://arxiv.org/abs/2310.03652
Data-driven constitutive modeling frameworks based on neural networks and classical representation theorems have recently gained considerable attention due to their ability to easily incorporate constitutive constraints and their excellent generaliza
Externí odkaz:
http://arxiv.org/abs/2308.11080
A novel data-driven constitutive modeling approach is proposed, which combines the physics-informed nature of modeling based on continuum thermodynamics with the benefits of machine learning. This approach is demonstrated on strain-rate-sensitive sof
Externí odkaz:
http://arxiv.org/abs/2304.13897
The development of accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges arise both in considering the appropriate model assumptions and from
Externí odkaz:
http://arxiv.org/abs/2210.08343
Anisotropy in the mechanical response of materials with microstructure is common and yet is difficult to assess and model. To construct accurate response models given only stress-strain data, we employ classical representation theory, novel neural ne
Externí odkaz:
http://arxiv.org/abs/2204.04529
Autor:
Fuhg, Jan N., van Wees, Lloyd, Obstalecki, Mark, Shade, Paul, Bouklas, Nikolaos, Kasemer, Matthew
The influence of the microstructure of a polycrystalline material on its macroscopic deformation response is still one of the major problems in materials engineering. For materials characterized by elastic-plastic deformation responses, predictive co
Externí odkaz:
http://arxiv.org/abs/2202.01885
Autor:
Wees, Lloyd van, Shankar, Karthik, Fuhg, Jan N., Bouklas, Nikolaos, Shade, Paul, Obstalecki, Mark, Kasemer, Matthew
Publikováno v:
In Materialia August 2024 36
