Failure trajectories, probable failure zones, and damage indices are some of the key
quantities of relevance in brittle fracture mechanics. High-fidelity numerical solvers that …

Abstract Machine learning (ML) has evolved as a technology used in even broader domains,
ranging from spam detection to space exploration, as a result of the boom in available data …

Structural systems are consistently encountering the variabilities in material properties,
undesirable defects and loading environments, which may potentially shorten their designed …

This paper presents a novel framework for predicting computer-aided engineering (CAE)
simulation results using machine learning (ML). The framework is applied to finite element …

Excessive medical waste is generated in various medical facilities, especially post-Covid.
Recently, sterilization-based shredding systems are being widely used to treat medical …

A machine learning based strategy is proposed for creating parametric surrogate models
from parametrized finite element model simulation results. In the first major step, a unified …

Phase-field models (PFMs) have proven to accurately predict complex crack patterns such
as crack branching, merging, and crack fragmentation, but they are computationally costly …

In this work, artificial neural network (ANN), a machine learning technique is applied in the
field of stress recovery in finite element analysis (FEA). The most crucial design factor in …

This paper introduces a novel computational framework, Finite Element Method Integrated
Networks (FEMIN), designed to accelerate crash simulations significantly. The core …

During the thermomechanical processing of titanium alloys in the β-domain, the β-phase
undergoes restoration phenomena. This work describes them by a mean-field physical …