62 citations · 198 across the 13 of their papers we have counts for
4 papers · 1 filter
A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks
Roberto Perera, Vinamra Agrawal
Despite their recent success, machine learning (ML) models such as graph neural networks (GNNs), suffer from drawbacks such as the need for large training datasets and poor perform…
Investigating shock wave propagation, evolution, and anisotropy using a moving window concurrent atomistic-continuum framework
Alexander S. Davis, Vinamra Agrawal
Despite their success in microscale modeling of materials, atomistic methods are still limited by short time scales, small domain sizes, and high strain rates. Multiscale formulati…
Dynamic and adaptive mesh-based graph neural network framework for simulating displacement and crack fields in phase field models
Roberto Perera, Vinamra Agrawal
Fracture is one of the main causes of failure in engineering structures. Phase field methods coupled with adaptive mesh refinement (AMR) techniques have been widely used to model c…
Graph neural networks for simulating crack coalescence and propagation in brittle materials
Roberto Perera, Davide Guzzetti, Vinamra Agrawal
High-fidelity fracture mechanics simulations of multiple microcracks interaction via physics-based models quickly become computationally expensive as the number of microcracks incr…