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Found 6 papers, 2 papers with code
PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling
We extend the capabilities of PARC to simulate unsteady, transient, and advection-dominant systems.
Challenges and opportunities for machine learning in multiscale computational modeling
Recently, machine learning (ML) has emerged as a promising solution that can either serve as a surrogate for, accelerate or augment traditional numerical methods.
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions
Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation.
A physics-aware deep learning model for energy localization in multiscale shock-to-detonation simulations of heterogeneous energetic materials
We introduce a new approach for SDT simulation by using deep learning to model the mesoscale energy localization of shock-initiated EM microstructures.
PARC: Physics-Aware Recurrent Convolutional Neural Networks to Assimilate Meso-scale Reactive Mechanics of Energetic Materials
The thermo-mechanical response of shock-initiated energetic materials (EM) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructure in a "materials-by-design" framework.
Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials
Emerging multi-scale predictive models of HE response to loads account for the physics at the meso-scale, i. e. at the scale of statistically representative clusters of particles and other features in the microstructure.
