62 citations · 111 across the 6 of their papers we have counts for
6 papers
Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations
Roberto Perera, Vinamra Agrawal
Mesh-based Graph Neural Networks (GNNs) have recently shown capabilities to simulate complex multiphysics problems with accelerated performance times. However, mesh-based GNNs requ…
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…
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…
Shedding some light on Light Up with Artificial Intelligence
Libo Sun, James Browning, Roberto Perera
The Light-Up puzzle, also known as the AKARI puzzle, has never been solved using modern artificial intelligence (AI) methods. Currently, the most widely used computational techniqu…
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…
Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images
Roberto Perera, Davide Guzzetti, Vinamra Agrawal
Additively manufactured metals exhibit heterogeneous microstructure which dictates their material and failure properties. Experimental microstructural characterization techniques g…