Roberto Perera
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Convolutional and Graph Neural Network Framework for Predicting Critical Impact Velocity in Heterogeneous PBX‐9501 Open
Heterogeneous energetic materials (HEM) can involve structural defects such as randomly distributed pores of varying size and shape. The unique arrangements of these defects cause initiation metrics such as pressure, temperature, and parti…
Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations Open
Mesh-based Graph Neural Networks (GNNs) have recently shown capabilities to simulate complex multiphysics problems with accelerated performance times. However, mesh-based GNNs require a large number of message-passing (MP) steps and suffer…
A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks Open
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 performance for unseen cases. In this work, we use transfer learni…
Dynamic and adaptive mesh-based graph neural network framework for simulating displacement and crack fields in phase field models Open
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 crack propagation due to their ease of implementation and sc…
View article: Shedding some light on Light Up with Artificial Intelligence
Shedding some light on Light Up with Artificial Intelligence Open
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 technique to autonomously develop solutions involve evolution theor…
Graph neural networks for emulating crack coalescence and propagation in brittle materials Open
High-fidelity fracture mechanics simulations of multiple microcracks interaction via physics based models quickly become computationally prohibitive as the number of microcracks increases. This work develops a Graph Neural Network (GNN) ba…