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Found 5 papers, 1 papers with code
A deep learning energy method for hyperelasticity and viscoelasticity
The potential energy formulation and deep learning are merged to solve partial differential equations governing the deformation in hyperelastic and viscoelastic materials.
Meshless physics-informed deep learning method for three-dimensional solid mechanics
We show that the DCM can capture the response qualitatively and quantitatively, without the need for any data generation using other numerical methods such as the FEM.
Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure
Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era.
Topology optimization of 2D structures with nonlinearities using deep learning
We have considered the case of materials with a linear elastic response with and without stress constraint.
Review and Examination of Input Feature Preparation Methods and Machine Learning Models for Turbulence Modeling
Model extrapolation to unseen flow is one of the biggest challenges facing data-driven turbulence modeling, especially for models with high dimensional inputs that involve many flow features.
Fluid Dynamics Computational Physics
