4 citations · 5 across the 12 of their papers we have counts for
4 papers · 1 filter
Diff-NekRS: A Scalable Differentiable Framework for Multi-Timestep Solver-in-the-Loop Training
Junoh Jung, Riccardo Balin, Bethany Lusch +1
Hybrid physics-machine-learning solvers improve under-resolved simulations by embedding trainable corrections into the time integration. During autoregressive inference, repeated s…
PythonFOAM: In-situ data analyses with OpenFOAM and Python
Romit Maulik, Dimitrios Fytanidis, Bethany Lusch +2
We outline the development of a general-purpose Python-based data analysis tool for OpenFOAM. Our implementation relies on the construction of OpenFOAM applications that have bindi…
Deploying deep learning in OpenFOAM with TensorFlow
Romit Maulik, Himanshu Sharma, Saumil Patel +2
We outline the development of a data science module within OpenFOAM which allows for the in-situ deployment of trained deep learning architectures for general-purpose predictive ta…
Recurrent Neural Network Architecture Search for Geophysical Emulation
Romit Maulik, Romain Egele, Bethany Lusch +1
Developing surrogate geophysical models from data is a key research topic in atmospheric and oceanic modeling because of the large computational costs associated with numerical sim…