Nowadays computers can perform symbolic calculations––in addition to mere numerical
computations for which they were originally designed––and this opens up exciting …

Integration of machine learning (ML) with topology optimization (TO) has been attempted in
many works. However, most works employ ML in a data-driven paradigm, which requires …

Advances in additive manufacturing enable the fabrication of complex structures with
intricate geometric details, which bring opportunities for high-resolution structure design and …

We propose a data-driven framework to increase the computational efficiency of the explicit
finite element method in the structural analysis of soft tissue. An encoder–decoder long short …

By decomposing the strain part of the conforming 10-node tetrahedral element T10 and
relaxing the compatibility condition for the constant strain term, the performance of the …

It is known that the standard full integration ten node tetrahedral element is inaccurate for
thin nearly incompressible structures. Also, the commercial code ABAQUS recommends …

Advances in additive manufacturing enable the fabrication of complex structures with
intricate geometric details. It also escalates the potential for high-resolution structure design …

The use of closed-form expressions in a finite element code significantly reduces the time
required to evaluate the element stiffness matrix as compared to Gaussian quadrature. The …

High-resolution structural designs attracts researchers to multi-scale topology optimizations
(TO) paradigms. With the advances of machine learning (ML) methods, the integration of ML …

An automatic adaptive refinement procedure for 3D finite element analysis is presented.
Based on a carefully designed procedure to eliminate the instability of the superconvergent …