Asymmetric elastoplasticity of stacked graphene assembly actualizes programmable untethered soft robotics [PDF]
Nature Communications, 2020 Developing programmable untethered soft robotics remains a challenge. Here the authors apply the asymmetric elastoplasticity of stacked graphene assembly to address this challenge and realize untethered thermoresponsive morphing in tandem with high ...
Shuai Wang +10 more
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A data-driven reduced-order surrogate model for entire elastoplastic simulations applied to representative volume elements [PDF]
Scientific Reports, 2023 This contribution discusses surrogate models that emulate the solution field(s) in the entire simulation domain. The surrogate uses the most characteristic modes of the solution field(s), in combination with neural networks to emulate the coefficients of
S. Vijayaraghavan +5 more
doaj +2 more sources
Asymptotic numerical method for hyperelasticity and elastoplasticity: a review [PDF]
Proceedings of the Royal Society AThe literature about the asymptotic numerical method (ANM) is reviewed in this paper as well as its application to hyperelasticity and elastoplasticity. ANM is a generic continuation method based on the computation of Taylor series for solving nonlinear ...
Michel Potier-Ferry
semanticscholar +2 more sources
An Eulerian projection method for quasi-static elastoplasticity [PDF]
Journal of Computational Physics, 2015 A well-established numerical approach to solve the Navier--Stokes equations for incompressible fluids is Chorin's projection method, whereby the fluid velocity is explicitly updated, and then an elliptic problem for the pressure is solved, which is used ...
Bouchbinder, Eran +2 more
core +6 more sources
Integration algorithms of elastoplasticity for ceramic powder compaction [PDF]
, 2014 Inelastic deformation of ceramic powders (and of a broad class of rock-like and granular materials), can be described with the yield function proposed by Bigoni and Piccolroaz (2004, Yield criteria for quasibrittle and frictional materials. Int.
Argani, L. +3 more
core +3 more sources
Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions [PDF]
Computer Methods in Applied Mechanics and Engineering, 2023 Conventional neural network elastoplasticity models are often perceived as lacking interpretability. This paper introduces a two-step machine learning approach that returns mathematical models interpretable by human experts. In particular, we introduce a
B. Bahmani, H. S. Suh, Waiching Sun
semanticscholar +1 more source
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture [PDF]
ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia, 2023 We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture. State-of-the-art scientific computing models like the Material Point Method (MPM) faithfully simulate large-deformation elastoplasticity
Zeshun Zong +8 more
semanticscholar +1 more source
A deep learning energy-based method for classical elastoplasticity [PDF]
International journal of plasticity, 2022 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.
Junyan He +4 more
semanticscholar +1 more source
Modular machine learning-based elastoplasticity: generalization in the context of limited data [PDF]
Computer Methods in Applied Mechanics and Engineering, 2022 The development of accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics.
J. Fuhg +4 more
semanticscholar +1 more source
Elastoplastic Analysis of Frame Structures Using Radial Point Interpolation Meshless Methods
Applied Sciences, 2023 The need to design structures and structural elements that are more efficient in terms of performance is a key aspect of engineering. For a given material to be used at its maximum capacity, considering non-linear characteristics is mandatory.
Jorge Belinha +2 more
doaj +1 more source