127 citations · 762 across the 47 of their papers we have counts for
6 papers · 1 filter
Towards learning Lattice Boltzmann collision operators
Alessandro Corbetta, Alessandro Gabbana, Vitaliy Gyrya +3
In this work we explore the possibility of learning from data collision operators for the Lattice Boltzmann Method using a deep learning approach. We compare a hierarchy of designs…
Physics-Constrained Generative Adversarial Networks for 3D Turbulence
Dima Tretiak, Arvind T. Mohan, Daniel Livescu
Generative Adversarial Networks (GANs) have received wide acclaim among the machine learning (ML) community for their ability to generate realistic 2D images. ML is being applied m…
Interpreting neural network models of residual scalar flux
Gavin D. Portwood, Balasubramanya T. Nadiga, Juan A. Saenz +1
We show that in addition to providing effective and competitive closures, when analysed in terms of dynamics and physically-relevant diagnostics, artificial neural networks (ANNs)…
Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence
Arvind T. Mohan, Nicholas Lubbers, Daniel Livescu +1
In the recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences. A major challenge in deep learning of PDEs is enforcing…
Time-series learning of latent-space dynamics for reduced-order model closure
Romit Maulik, Arvind Mohan, Bethany Lusch +3
We study the performance of long short-term memory networks (LSTMs) and neural ordinary differential equations (NODEs) in learning latent-space representations of dynamical equatio…
Leveraging Bayesian Analysis To Improve Accuracy of Approximate Models
Balasubramanya T. Nadiga, Chiyu Jiang, Daniel Livescu
We focus on improving the accuracy of an approximate model of a multiscale dynamical system that uses a set of parameter-dependent terms to account for the effects of unresolved or…