187 citations · 202 across the 15 of their papers we have counts for
9 papers · 1 filter
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…
Distributed deep reinforcement learning for simulation control
Suraj Pawar, Romit Maulik
Several applications in the scientific simulation of physical systems can be formulated as control/optimization problems. The computational models for such systems generally contai…
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation
Romit Maulik, Themistoklis Botsas, Nesar Ramachandra +2
Non-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerg…
Non-autoregressive time-series methods for stable parametric reduced-order models
Romit Maulik, Bethany Lusch, Prasanna Balaprakash
Advection-dominated dynamical systems, characterized by partial differential equations, are found in applications ranging from weather forecasting to engineering design where accur…
A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities
Vishwas Rao, Romit Maulik, Emil Constantinescu +1
We develop a novel computational method for evaluating the extreme excursion probabilities arising from random initialization of nonlinear dynamical systems. The method uses excurs…