Zobrazeno 1 - 10
of 317
pro vyhledávání: '"Kochmann, Dennis M."'
Architected materials achieve unique mechanical properties through precisely engineered microstructures that minimize material usage. However, a key challenge of low-density materials is balancing high stiffness with stable deformability up to large
Externí odkaz:
http://arxiv.org/abs/2409.12652
Publikováno v:
Extreme Mechanics Letters 72 (2024) 102243
We introduce HyperCAN, a machine learning framework that utilizes hypernetworks to construct adaptable constitutive artificial neural networks for a wide range of beam-based metamaterials exhibiting diverse mechanical behavior under finite deformatio
Externí odkaz:
http://arxiv.org/abs/2408.06017
The nucleation and propagation of disconnections play an essential role during twin growth. Atomistic methods can reveal such small structural features on twin facets and model their motion, yet are limited by the simulation length and time scales. A
Externí odkaz:
http://arxiv.org/abs/2405.14050
Generative models such as denoising diffusion models are quickly advancing their ability to approximate highly complex data distributions. They are also increasingly leveraged in scientific machine learning, where samples from the implied data distri
Externí odkaz:
http://arxiv.org/abs/2403.14404
Predicting the mechanics of large structural networks, such as beam-based architected materials, requires a multiscale computational strategy that preserves information about the discrete structure while being applicable to large assemblies of struts
Externí odkaz:
http://arxiv.org/abs/2403.09495
Grain boundary (GB) properties greatly influence the mechanical, electrical, and thermal response of polycrystalline materials. Most computational studies of GB properties at finite temperatures use molecular dynamics (MD), which is computationally e
Externí odkaz:
http://arxiv.org/abs/2402.12247
Autor:
Dorn, Charles, Kochmann, Dennis M.
Although metamaterials have been widely used for controlling elastic waves through bandgap engineering, the directed guidance of stress waves in non-periodic structures has remained a challenge. This work demonstrates that spatially graded metamateri
Externí odkaz:
http://arxiv.org/abs/2306.16240
Publikováno v:
Nat Commun 14, 7563 (2023)
The rise of machine learning has fueled the discovery of new materials and, especially, metamaterials--truss lattices being their most prominent class. While their tailorable properties have been explored extensively, the design of truss-based metama
Externí odkaz:
http://arxiv.org/abs/2306.14773
The accelerated inverse design of complex material properties - such as identifying a material with a given stress-strain response over a nonlinear deformation path - holds great potential for addressing challenges from soft robotics to biomedical im
Externí odkaz:
http://arxiv.org/abs/2305.19836
Overcoming the time scale limitations of atomistics can be achieved by switching from the state-space representation of Molecular Dynamics (MD) to a statistical-mechanics-based representation in phase space, where approximations such as maximum-entro
Externí odkaz:
http://arxiv.org/abs/2303.16088