Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Vlassis, Nikolaos N."'
Microstructure plays a critical role in determining the macroscopic properties of materials, with applications spanning alloy design, MEMS devices, and tissue engineering, among many others. Computational frameworks have been developed to capture the
Externí odkaz:
http://arxiv.org/abs/2409.14473
Autor:
Fuhg, Jan Niklas, Padmanabha, Govinda Anantha, Bouklas, Nikolaos, Bahmani, Bahador, Sun, WaiChing, Vlassis, Nikolaos N., Flaschel, Moritz, Carrara, Pietro, De Lorenzis, Laura
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-dependent response of solids. Our objective is to provide an organized tax
Externí odkaz:
http://arxiv.org/abs/2405.03658
The shapes and morphological features of grains in sand assemblies have far-reaching implications in many engineering applications, such as geotechnical engineering, computer animations, petroleum engineering, and concentrated solar power. Yet, our u
Externí odkaz:
http://arxiv.org/abs/2306.04411
Autor:
Vlassis, Nikolaos N., Sun, WaiChing
In this paper, we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generative models that use diffusion-based dynamics to gradually denoise images
Externí odkaz:
http://arxiv.org/abs/2302.12881
Autor:
Villarreal, Ruben, Vlassis, Nikolaos N., Phan, Nhon N., Catanach, Tommie A., Jones, Reese E., Trask, Nathaniel A., Kramer, Sharlotte L. B., Sun, WaiChing
Experimental data is costly to obtain, which makes it difficult to calibrate complex models. For many models an experimental design that produces the best calibration given a limited experimental budget is not obvious. This paper introduces a deep re
Externí odkaz:
http://arxiv.org/abs/2209.13126
Autor:
Vlassis, Nikolaos N., Sun, WaiChing
The history-dependent behaviors of classical plasticity models are often driven by internal variables evolved according to phenomenological laws. The difficulty to interpret how these internal variables represent a history of deformation, the lack of
Externí odkaz:
http://arxiv.org/abs/2208.00246
We present a machine learning framework to train and validate neural networks to predict the anisotropic elastic response of the monoclinic organic molecular crystal $\beta$-HMX in the geometrical nonlinear regime. A filtered molecular dynamic (MD) s
Externí odkaz:
http://arxiv.org/abs/2112.02077
This paper presents a computational framework that generates ensemble predictive mechanics models with uncertainty quantification (UQ). We first develop a causal discovery algorithm to infer causal relations among time-history data measured during ea
Externí odkaz:
http://arxiv.org/abs/2105.09980
Autor:
Vlassis, Nikolaos N., Sun, WaiChing
We introduce a deep learning framework designed to train smoothed elastoplasticity models with interpretable components, such as a smoothed stored elastic energy function, a yield surface, and a plastic flow that are evolved based on a set of deep ne
Externí odkaz:
http://arxiv.org/abs/2010.11265
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