303 citations · 2.2k across the 86 of their papers we have counts for
14 papers · 1 filter
Probabilistic neural network-based reduced-order surrogate for fluid flows
Kai Fukami, Romit Maulik, Nesar Ramachandra +2
In recent years, there have been a surge in applications of neural networks (NNs) in physical sciences. Although various algorithmic advances have been proposed, there are, thus fa…
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…
Meta-modeling strategy for data-driven forecasting
Dominic J. Skinner, Romit Maulik
Accurately forecasting the weather is a key requirement for climate change mitigation. Data-driven methods offer the ability to make more accurate forecasts, but lack interpretabil…
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…
Fast neural Poincaré maps for toroidal magnetic fields
J. W. Burby, Q. Tang, R. Maulik
Poincaré maps for toroidal magnetic fields are routinely employed to study gross confinement properties in devices built to contain hot plasmas. In most practical applications, eva…
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…
Probabilistic neural networks for fluid flow surrogate modeling and data recovery
Romit Maulik, Kai Fukami, Nesar Ramachandra +2
We consider the use of probabilistic neural networks for fluid flow {surrogate modeling} and data recovery. This framework is constructed by assuming that the target variables are…
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…
Determining feature importance for actionable climate change mitigation policies
Romit Maulik, Junghwa Choi, Wesley Wehde +1
Given the importance of public support for policy change and implementation, public policymakers and researchers have attempted to understand the factors associated with this suppo…
Neural network representability of fully ionized plasma fluid model closures
Romit Maulik, Nathan A. Garland, Xian-Zhu Tang +1
The closure problem in fluid modeling is a well-known challenge to modelers aiming to accurately describe their system of interest. Over many years, analytic formulations in a wide…
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
Romit Maulik, Bethany Lusch, Prasanna Balaprakash
A common strategy for the dimensionality reduction of nonlinear partial differential equations relies on the use of the proper orthogonal decomposition (POD) to identify a reduced…
Site-specific graph neural network for predicting protonation energy of oxygenate molecules
Romit Maulik, Rajeev Surendran Array, Prasanna Balaprakash
Bio-oil molecule assessment is essential for the sustainable development of chemicals and transportation fuels. These oxygenated molecules have adequate carbon, hydrogen, and oxyge…