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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
Abstract Partial Differential Equations (PDEs) are fundamental to model different
phenomena in science and engineering mathematically. Solving them is a crucial step …
phenomena in science and engineering mathematically. Solving them is a crucial step …
Deep learning for plasticity and thermo-viscoplasticity
Predicting history-dependent materials' responses is crucial, as path-dependent behavior
appears while characterizing or geometrically designing many materials (eg, metallic and …
appears while characterizing or geometrically designing many materials (eg, metallic and …
Meshless physics‐informed deep learning method for three‐dimensional solid mechanics
Deep learning (DL) and the collocation method are merged and used to solve partial
differential equations (PDEs) describing structures' deformation. We have considered …
differential equations (PDEs) describing structures' deformation. We have considered …
Enhanced physics‐informed neural networks for hyperelasticity
Physics‐informed neural networks have gained growing interest. Specifically, they are used
to solve partial differential equations governing several physical phenomena. However …
to solve partial differential equations governing several physical phenomena. However …
The magneto-electro-elastic coupling isogeometric analysis method for the static and dynamic analysis of magneto-electro-elastic structures under thermal loading
L Zhou, F Qu - Composite Structures, 2023 - Elsevier
Many engineering problems show some degree of coupling or interaction between different
physics fields. It is necessary to improve the calculation efficiency and accuracy for magneto …
physics fields. It is necessary to improve the calculation efficiency and accuracy for magneto …
An analytical solution for vibration response of CNT/GPL/fibre/polymer hybrid composite micro/nanoplates
In the present article, a closed-form solution is carried out for the vibration response of
CNT/GPL/fiber/polymer hybrid composite macro/micro/nanoplates resting on elastic support …
CNT/GPL/fiber/polymer hybrid composite macro/micro/nanoplates resting on elastic support …
Deep learning modeling strategy for material science: from natural materials to metamaterials
Computational modeling is a crucial approach in material-related research for discovering
new materials with superior properties. However, the high design flexibility in materials …
new materials with superior properties. However, the high design flexibility in materials …
Explicit error estimates for spline approximation of arbitrary smoothness in isogeometric analysis
In this paper we provide a priori error estimates with explicit constants for both the L^ 2 L 2-
projection and the Ritz projection onto spline spaces of arbitrary smoothness defined on …
projection and the Ritz projection onto spline spaces of arbitrary smoothness defined on …
Improving the accuracy of the deep energy method
The deep energy method (DEM), a type of physics-informed neural network, is evolving as
an alternative to finite element analysis. It employs the principle of minimum potential energy …
an alternative to finite element analysis. It employs the principle of minimum potential energy …
Collaborative design of fiber path and shape for complex composite shells based on isogeometric analysis
Composite shells with complex geometry are widely used in aerospace structures. Due to
the complexity of geometry and curvilinear fiber path, the analysis and optimization based …
the complexity of geometry and curvilinear fiber path, the analysis and optimization based …