Zobrazeno 1 - 10
of 167
pro vyhledávání: '"He Junyan"'
Autor:
Park, Jaewan, Kushwaha, Shashank, He, Junyan, Koric, Seid, Liu, Qibang, Jasiuk, Iwona, Abueidda, Diab
Metamaterials, synthetic materials with customized properties, have emerged as a promising field due to advancements in additive manufacturing. These materials derive unique mechanical properties from their internal lattice structures, which are ofte
Externí odkaz:
http://arxiv.org/abs/2409.13908
Unlike classical artificial neural networks, which require retraining for each new set of parametric inputs, the Deep Operator Network (DeepONet), a lately introduced deep learning framework, approximates linear and nonlinear solution operators by ta
Externí odkaz:
http://arxiv.org/abs/2403.14795
Modern digital engineering design process commonly involves expensive repeated simulations on varying three-dimensional (3D) geometries. The efficient prediction capability of neural networks (NNs) makes them a suitable surrogate to provide design in
Externí odkaz:
http://arxiv.org/abs/2403.14788
Crystal plasticity (CP) simulations are a tool for understanding how microstructure morphology and texture affect mechanical properties and are an essential component of elucidating the structure-property relations. However, it can be computationally
Externí odkaz:
http://arxiv.org/abs/2401.09977
The Deep Operator Network (DeepONet) structure has shown great potential in approximating complex solution operators with low generalization errors. Recently, a sequential DeepONet (S-DeepONet) was proposed to use sequential learning models in the br
Externí odkaz:
http://arxiv.org/abs/2311.11500
Biological structural designs in nature, like hoof walls, horns, and antlers, can be used as inspiration for generating structures with excellent mechanical properties. A common theme in these designs is the small percent porosity in the structure ra
Externí odkaz:
http://arxiv.org/abs/2307.00986
Deep Operator Network (DeepONet), a recently introduced deep learning operator network, approximates linear and nonlinear solution operators by taking parametric functions (infinite-dimensional objects) as inputs and mapping them to solution function
Externí odkaz:
http://arxiv.org/abs/2306.08218
A novel deep operator network (DeepONet) with a residual U-Net (ResUNet) as the trunk network is devised to predict full-field highly nonlinear elastic-plastic stress response for complex geometries obtained from topology optimization under variable
Externí odkaz:
http://arxiv.org/abs/2306.03645
The deep energy method (DEM) has been used to solve the elastic deformation of structures with linear elasticity, hyperelasticity, and strain-gradient elasticity material models based on the principle of minimum potential energy. In this work, we ext
Externí odkaz:
http://arxiv.org/abs/2209.06467
A graph neural network (GCN) is employed in the deep energy method (DEM) model to solve the momentum balance equation in 3D for the deformation of linear elastic and hyperelastic materials due to its ability to handle irregular domains over the tradi
Externí odkaz:
http://arxiv.org/abs/2207.07216