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2020 – today
- 2022
[j8]S. Ashwin Renganathan
, Romit Maulik, Stefano Letizia, Giacomo Valerio Iungo:
Data-driven wind turbine wake modeling via probabilistic machine learning. Neural Comput. Appl. 34(8): 6171-6186 (2022)- [i15]Sahil Bhola, Suraj Pawar, Prasanna Balaprakash, Romit Maulik:
Multi-fidelity reinforcement learning framework for shape optimization. CoRR abs/2202.11170 (2022) - [i14]Alec J. Linot, Josh W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik:
Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems. CoRR abs/2203.15706 (2022) - 2021
- [d1]Gianmarco Mengaldo, Romit Maulik:
PySPOD: A Python package for Spectral Proper Orthogonal Decomposition (SPOD). J. Open Source Softw. 6(60): 2862 (2021) - [j7]Suraj Pawar, Romit Maulik:
Distributed deep reinforcement learning for simulation control. Mach. Learn. Sci. Technol. 2(2): 25029 (2021) - [j6]Kai Fukami, Romit Maulik, Nesar Ramachandra, Koji Fukagata, Kunihiko Taira:
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning. Nat. Mach. Intell. 3(11): 945-951 (2021) - [c4]Varuni Katti Sastry, Romit Maulik, Vishwas Rao, Bethany Lusch, S. Ashwin Renganathan, Rao Kotamarthi:
Data-Driven Deep Learning Emulators for Geophysical Forecasting. ICCS (5) 2021: 433-446 - [c3]Romit Maulik, Gianmarco Mengaldo:
PyParSVD: A streaming, distributed and randomized singular-value-decomposition library. DRBSD@SC 2021: 19-25 - [i13]Kai Fukami, Romit Maulik, Nesar Ramachandra, Koji Fukagata, Kunihiko Taira:
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning. CoRR abs/2101.00554 (2021) - [i12]Romit Maulik, Dimitrios Fytanidis, Bethany Lusch, Venkatram Vishwanath, Saumil Patel:
PythonFOAM: In-situ data analyses with OpenFOAM and Python. CoRR abs/2103.09389 (2021) - [i11]Yubin Lu, Romit Maulik, Ting Gao, Felix Dietrich, Ioannis G. Kevrekidis, Jinqiao Duan:
Learning the temporal evolution of multivariate densities via normalizing flows. CoRR abs/2107.13735 (2021) - [i10]Romit Maulik, Gianmarco Mengaldo:
PyParSVD: A streaming, distributed and randomized singular-value-decomposition library. CoRR abs/2108.08845 (2021) - [i9]S. Ashwin Renganathan, Romit Maulik, Stefano Letizia, Giacomo Valerio Iungo:
Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning. CoRR abs/2109.02411 (2021) - [i8]Masaki Morimoto, Kai Fukami, Romit Maulik, Ricardo Vinuesa, Koji Fukagata:
Assessments of model-form uncertainty using Gaussian stochastic weight averaging for fluid-flow regression. CoRR abs/2109.08248 (2021) - [i7]Andrea Lario, Romit Maulik, Gianluigi Rozza, Gianmarco Mengaldo:
Neural-network learning of SPOD latent dynamics. CoRR abs/2110.09218 (2021) - [i6]Romain Egele, Romit Maulik, Krishnan Raghavan, Prasanna Balaprakash, Bethany Lusch:
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. CoRR abs/2110.13511 (2021) - 2020
- [j5]Romit Maulik, Omer San:
Numerical assessments of a parametric implicit large eddy simulation model. J. Comput. Appl. Math. 376: 112866 (2020) - [c2]Vishwas Rao, Romit Maulik, Emil M. Constantinescu, Mihai Anitescu:
A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities. ICCS (6) 2020: 169-182 - [c1]Romit Maulik, Romain Egele, Bethany Lusch, Prasanna Balaprakash:
Recurrent neural network architecture search for geophysical emulation. SC 2020: 8 - [i5]Romit Maulik, Rajeev Surendran Array, Prasanna Balaprakash:
Site-specific graph neural network for predicting protonation energy of oxygenate molecules. CoRR abs/2001.03136 (2020) - [i4]Romit Maulik, Themistoklis Botsas, Nesar Ramachandra, Lachlan Robert Mason, Indranil Pan:
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation. CoRR abs/2007.12167 (2020) - [i3]Dominic J. Skinner, Romit Maulik:
Meta-modeling strategy for data-driven forecasting. CoRR abs/2012.00678 (2020) - [i2]Romit Maulik, Himanshu Sharma, Saumil Patel, Bethany Lusch, Elise Jennings:
Deploying deep learning in OpenFOAM with TensorFlow. CoRR abs/2012.00900 (2020)
2010 – 2019
- 2019
- [j4]Omer San, Romit Maulik, Mansoor Ahmed:
An artificial neural network framework for reduced order modeling of transient flows. Commun. Nonlinear Sci. Numer. Simul. 77: 271-287 (2019) - [i1]Romit Maulik, Vishwas Rao, Sandeep Madireddy, Bethany Lusch, Prasanna Balaprakash:
Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models. CoRR abs/1909.09144 (2019) - 2018
- [j3]Omer San, Romit Maulik:
Neural network closures for nonlinear model order reduction. Adv. Comput. Math. 44(6): 1717-1750 (2018) - [j2]Romit Maulik, Omer San:
Explicit and implicit LES closures for Burgers turbulence. J. Comput. Appl. Math. 327: 12-40 (2018) - 2017
- [j1]Romit Maulik, Omer San:
A novel dynamic framework for subgrid scale parametrization of mesoscale eddies in quasigeostrophic turbulent flows. Comput. Math. Appl. 74(3): 420-445 (2017)
Coauthor Index
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