Romit Maulik

According to our database, Romit Maulik authored at least 53 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Reduced-order modeling on a near-term quantum computer.

Katherine Asztalos, René Steijl, Romit Maulik

J. Comput. Phys., 2024

Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package.

Marcin Rogowski, Brandon C. Y. Yeung, Oliver T. Schmidt, Romit Maulik, Lisandro Dalcín, Matteo Parsani, Gianmarco Mengaldo

Comput. Phys. Commun., 2024

A competitive baseline for deep learning enhanced data assimilation using conditional Gaussian ensemble Kalman filtering.

Zachariah Malik, Romit Maulik

CoRR, 2024

Measure-Theoretic Time-Delay Embedding.

Jonah Botvinick-Greenhouse, Maria Oprea, Romit Maulik, Yunan Yang

CoRR, 2024

Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks.

Shivam Barwey, Pinaki Pal, Saumil Patel, Riccardo Balin, Bethany Lusch, Venkatram Vishwanath, Romit Maulik, Ramesh Balakrishnan

CoRR, 2024

Higher order quantum reservoir computing for non-intrusive reduced-order models.

Vinamr Jain, Romit Maulik

CoRR, 2024

Divide And Conquer: Learning Chaotic Dynamical Systems With Multistep Penalty Neural Ordinary Differential Equations.

Dibyajyoti Chakraborty, Seung Whan Chung, Romit Maulik

CoRR, 2024

A note on the error analysis of data-driven closure models for large eddy simulations of turbulence.

Dibyajyoti Chakraborty, Shivam Barwey, Hong Zhang, Romit Maulik

CoRR, 2024

LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles.

Haiwen Guan, Troy Arcomano, Ashesh Chattopadhyay, Romit Maulik

CoRR, 2024

Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning.

Tyler Chang, Andrew Gillette, Romit Maulik

CoRR, 2024

Scientific machine learning for closure models in multiscale problems: a review.

Benjamin Sanderse, Panos Stinis, Romit Maulik, Shady E. Ahmed

CoRR, 2024

Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective.

Sunwoong Yang, Hojin Kim, Yoonpyo Hong, Kwanjung Yee, Romit Maulik, Namwoo Kang

CoRR, 2024

2023

Multiscale graph neural network autoencoders for interpretable scientific machine learning.

Shivam Barwey, Varun Shankar, Venkatasubramanian Viswanathan, Romit Maulik

J. Comput. Phys., December, 2023

Multi-fidelity reinforcement learning framework for shape optimization.

Sahil Bhola, Suraj Pawar, Prasanna Balaprakash, Romit Maulik

J. Comput. Phys., June, 2023

Differentiable physics-enabled closure modeling for Burgers' turbulence.

Varun Shankar, Vedant Puri, Ramesh Balakrishnan, Romit Maulik, Venkatasubramanian Viswanathan

Mach. Learn. Sci. Technol., March, 2023

Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems.

Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik

J. Comput. Phys., February, 2023

Physics-informed neural networks for mesh deformation with exact boundary enforcement.

Atakan Aygun, Romit Maulik, Ali Karakus

Eng. Appl. Artif. Intell., 2023

Scaling transformer neural networks for skillful and reliable medium-range weather forecasting.

Tung Nguyen, Rohan Shah, Hritik Bansal, Troy Arcomano, Sandeep Madireddy, Romit Maulik, Veerabhadra Kotamarthi, Ian T. Foster, Aditya Grover

CoRR, 2023

Interpretable Fine-Tuning for Graph Neural Network Surrogate Models.

Shivam Barwey, Romit Maulik

CoRR, 2023

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies.

Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens

CoRR, 2023

Generalizable improvement of the Spalart-Allmaras model through assimilation of experimental data.

Deepinder Jot Singh Aulakh, Romit Maulik

CoRR, 2023

Differentiable Turbulence II.

Varun Shankar, Romit Maulik, Venkatasubramanian Viswanathan

CoRR, 2023

Importance of equivariant and invariant symmetries for fluid flow modeling.

Varun Shankar, Shivam Barwey, Zico Kolter, Romit Maulik, Venkatasubramanian Viswanathan

CoRR, 2023

Differentiable Turbulence.

Varun Shankar, Romit Maulik, Venkatasubramanian Viswanathan

CoRR, 2023

Generative Modeling of Time-Dependent Densities via Optimal Transport and Projection Pursuit.

Jonah Botvinick-Greenhouse, Yunan Yang, Romit Maulik

CoRR, 2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles.

Romit Maulik, Romain Egele, Krishnan Raghavan, Prasanna Balaprakash

CoRR, 2023

2022

Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling.

Nathan A Garland, Romit Maulik, Qi Tang, Xian-Zhu Tang, Prasanna Balaprakash

Mach. Learn. Sci. Technol., December, 2022

Data-driven wind turbine wake modeling via probabilistic machine learning.

S. Ashwin Renganathan, Romit Maulik, Stefano Letizia, Giacomo Valerio Iungo

Neural Comput. Appl., 2022

PythonFOAM: In-situ data analyses with OpenFOAM and Python.

Romit Maulik, Dimitrios Fytanidis, Bethany Lusch, Venkatram Vishwanath, Saumil Patel

J. Comput. Sci., 2022

Neural-network learning of SPOD latent dynamics.

Andrea Lario, Romit Maulik, Oliver T. Schmidt, Gianluigi Rozza, Gianmarco Mengaldo

J. Comput. Phys., 2022

Modeling Wind Turbine Performance and Wake Interactions with Machine Learning.

Coleman Moss, Romit Maulik, Giacomo Valerio Iungo

CoRR, 2022

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.

Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash

Proceedings of the 26th International Conference on Pattern Recognition, 2022

2021

Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning.

Kai Fukami, Romit Maulik, Nesar Ramachandra, Koji Fukagata, Kunihiko Taira

Nat. Mach. Intell., 2021

Distributed deep reinforcement learning for simulation control.

Suraj Pawar, Romit Maulik

Mach. Learn. Sci. Technol., 2021

PySPOD: A Python package for Spectral Proper Orthogonal Decomposition (SPOD).

Gianmarco Mengaldo, Romit Maulik

J. Open Source Softw., 2021

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification.

Romain Egele, Romit Maulik, Krishnan Raghavan, Prasanna Balaprakash, Bethany Lusch

CoRR, 2021

Neural-network learning of SPOD latent dynamics.

Andrea Lario, Romit Maulik, Gianluigi Rozza, Gianmarco Mengaldo

CoRR, 2021

Assessments of model-form uncertainty using Gaussian stochastic weight averaging for fluid-flow regression.

Masaki Morimoto, Kai Fukami, Romit Maulik, Ricardo Vinuesa, Koji Fukagata

CoRR, 2021

Learning the temporal evolution of multivariate densities via normalizing flows.

Yubin Lu, Romit Maulik, Ting Gao, Felix Dietrich, Ioannis G. Kevrekidis, Jinqiao Duan

CoRR, 2021

PyParSVD: A streaming, distributed and randomized singular-value-decomposition library.

Romit Maulik, Gianmarco Mengaldo

Proceedings of the 2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, 2021

Data-Driven Deep Learning Emulators for Geophysical Forecasting.

Varuni Katti Sastry, Romit Maulik, Vishwas Rao, Bethany Lusch, S. Ashwin Renganathan, Rao Kotamarthi

Proceedings of the Computational Science - ICCS 2021, 2021

2020

Numerical assessments of a parametric implicit large eddy simulation model.

Romit Maulik, Omer San

J. Comput. Appl. Math., 2020

Deploying deep learning in OpenFOAM with TensorFlow.

Romit Maulik, Himanshu Sharma, Saumil Patel, Bethany Lusch, Elise Jennings

CoRR, 2020

Meta-modeling strategy for data-driven forecasting.

Dominic J. Skinner, Romit Maulik

CoRR, 2020

Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation.

Romit Maulik, Themistoklis Botsas, Nesar Ramachandra, Lachlan Robert Mason, Indranil Pan

CoRR, 2020

Site-specific graph neural network for predicting protonation energy of oxygenate molecules.

Romit Maulik, Rajeev Surendran Array, Prasanna Balaprakash

CoRR, 2020

Recurrent neural network architecture search for geophysical emulation.

Romit Maulik, Romain Egele, Bethany Lusch, Prasanna Balaprakash

Proceedings of the International Conference for High Performance Computing, 2020

A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities.

Vishwas Rao, Romit Maulik, Emil M. Constantinescu, Mihai Anitescu

Proceedings of the Computational Science - ICCS 2020, 2020

2019

Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models.

Romit Maulik, Vishwas Rao, Sandeep Madireddy, Bethany Lusch, Prasanna Balaprakash

CoRR, 2019

An artificial neural network framework for reduced order modeling of transient flows.

Omer San, Romit Maulik, Mansoor Ahmed

Commun. Nonlinear Sci. Numer. Simul., 2019

2018

Explicit and implicit LES closures for Burgers turbulence.

Romit Maulik, Omer San

J. Comput. Appl. Math., 2018

Neural network closures for nonlinear model order reduction.

Omer San, Romit Maulik

Adv. Comput. Math., 2018

2017

A novel dynamic framework for subgrid scale parametrization of mesoscale eddies in quasigeostrophic turbulent flows.

Romit Maulik, Omer San

Comput. Math. Appl., 2017