A novel dynamic framework for subgrid scale parametrization of mesoscale eddies in quasigeostrophic turbulent flows. - Romit Maulik; Omer San (Comput. Math. Appl., 2017)

Neural network closures for nonlinear model order reduction. - Omer San; Romit Maulik (Adv. Comput. Math., 2018)

Explicit and implicit LES closures for Burgers turbulence. - Romit Maulik; Omer San (J. Comput. Appl. Math., 2018)

An artificial neural network framework for reduced order modeling of transient flows. - Omer San; Romit Maulik; Mansoor Ahmed (Commun. Nonlinear Sci. Numer. Simul., 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)

A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities. - Vishwas Rao; Romit Maulik; Emil M. Constantinescu; Mihai Anitescu (ICCS (6), 2020)

Recurrent neural network architecture search for geophysical emulation. - Romit Maulik; Romain Egele; Bethany Lusch; Prasanna Balaprakash (SC, 2020)

Site-specific graph neural network for predicting protonation energy of oxygenate molecules. - Romit Maulik; Rajeev Surendran Array; Prasanna Balaprakash (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)

Meta-modeling strategy for data-driven forecasting. - Dominic J. Skinner; Romit Maulik (CoRR, 2020)

Deploying deep learning in OpenFOAM with TensorFlow. - Romit Maulik; Himanshu Sharma; Saumil Patel; Bethany Lusch; Elise Jennings (CoRR, 2020)

Numerical assessments of a parametric implicit large eddy simulation model. - Romit Maulik; Omer San (J. Comput. Appl. Math., 2020)

Data-Driven Deep Learning Emulators for Geophysical Forecasting. - Varuni Katti Sastry; Romit Maulik; Vishwas Rao; Bethany Lusch; S. Ashwin Renganathan; Rao Kotamarthi (ICCS (5), 2021)

PyParSVD: A streaming, distributed and randomized singular-value-decomposition library. - Romit Maulik; Gianmarco Mengaldo (DRBSD@SC, 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)

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)

Neural-network learning of SPOD latent dynamics. - Andrea Lario; Romit Maulik; Gianluigi Rozza; Gianmarco Mengaldo (CoRR, 2021)

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. - Romain Egele; Romit Maulik; Krishnan Raghavan; Prasanna Balaprakash; Bethany Lusch (CoRR, 2021)

PySPOD: A Python package for Spectral Proper Orthogonal Decomposition (SPOD). - Gianmarco Mengaldo; Romit Maulik (J. Open Source Softw., 2021)

Distributed deep reinforcement learning for simulation control. - Suraj Pawar; Romit Maulik (Mach. Learn. Sci. Technol., 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)

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification. - Romain Egele; Romit Maulik; Krishnan Raghavan; Bethany Lusch; Isabelle Guyon; Prasanna Balaprakash (ICPR, 2022)

Modeling Wind Turbine Performance and Wake Interactions with Machine Learning. - Coleman Moss; Romit Maulik; Giacomo Valerio Iungo (CoRR, 2022)

Neural-network learning of SPOD latent dynamics. - Andrea Lario; Romit Maulik; Oliver T. Schmidt; Gianluigi Rozza; Gianmarco Mengaldo (J. Comput. Phys., 2022)

PythonFOAM: In-situ data analyses with OpenFOAM and Python. - Romit Maulik; Dimitrios Fytanidis; Bethany Lusch; Venkatram Vishwanath; Saumil Patel (J. Comput. Sci., 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)

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 001; Xian-Zhu Tang; Prasanna Balaprakash (Mach. Learn. Sci. Technol., 2022)

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)

Generative Modeling of Time-Dependent Densities via Optimal Transport and Projection Pursuit. - Jonah Botvinick-Greenhouse; Yunan Yang; Romit Maulik (CoRR, 2023)

Differentiable Turbulence. - 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 II. - Varun Shankar; Romit Maulik; Venkatasubramanian Viswanathan (CoRR, 2023)

Generalizable improvement of the Spalart-Allmaras model through assimilation of experimental data. - Deepinder Jot Singh Aulakh; 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 004; Chengming Zhang 006; Masahiro Tanaka; Xiaoxia Wu; Jeff Rasley; Ammar Ahmad Awan; Connor Holmes; Martin Cai; et al. (CoRR, 2023)

Interpretable Fine-Tuning for Graph Neural Network Surrogate Models. - Shivam Barwey; Romit Maulik (CoRR, 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)

Physics-informed neural networks for mesh deformation with exact boundary enforcement. - Atakan Aygun; Romit Maulik; Ali Karakus (Eng. Appl. Artif. Intell., 2023)

Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems. - Alec J. Linot; Joshua W. Burby; Qi Tang 001; Prasanna Balaprakash; Michael D. Graham; Romit Maulik (J. Comput. Phys., 2023)

Differentiable physics-enabled closure modeling for Burgers' turbulence. - Varun Shankar; Vedant Puri; Ramesh Balakrishnan; Romit Maulik; Venkatasubramanian Viswanathan (Mach. Learn. Sci. Technol., 2023)

Multi-fidelity reinforcement learning framework for shape optimization. - Sahil Bhola; Suraj Pawar; Prasanna Balaprakash; Romit Maulik (J. Comput. Phys., 2023)

Multiscale graph neural network autoencoders for interpretable scientific machine learning. - Shivam Barwey; Varun Shankar; Venkatasubramanian Viswanathan; Romit Maulik (J. Comput. Phys., 2023)

Scaling transformer neural networks for skillful and reliable medium-range weather forecasting. - Tung Nguyen; Rohan Shah; Hritik Bansal; Troy Arcomano; Romit Maulik; Rao Kotamarthi; Ian T. Foster; Sandeep Madireddy; Aditya Grover (NeurIPS, 2024)

Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling. - Shivam Barwey; Riccardo Balin; Bethany Lusch; Saumil Patel; Ramesh Balakrishnan; Pinaki Pal; Romit Maulik; Venkatram Vishwanath (SC Workshops, 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)

Scientific machine learning for closure models in multiscale problems: a review. - Benjamin Sanderse; Panos Stinis; Romit Maulik; Shady E. Ahmed (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)

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)

Divide And Conquer: Learning Chaotic Dynamical Systems With Multistep Penalty Neural Ordinary Differential Equations. - Dibyajyoti Chakraborty; Seung Whan Chung; Romit Maulik (CoRR, 2024)

Higher order quantum reservoir computing for non-intrusive reduced-order models. - Vinamr Jain; Romit Maulik (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)

Measure-Theoretic Time-Delay Embedding. - Jonah Botvinick-Greenhouse; Maria Oprea; Romit Maulik; Yunan Yang (CoRR, 2024)

A competitive baseline for deep learning enhanced data assimilation using conditional Gaussian ensemble Kalman filtering. - Zachariah Malik; Romit Maulik (CoRR, 2024)

Improved deep learning of chaotic dynamical systems with multistep penalty losses. - Dibyajyoti Chakraborty; Seung Whan Chung; Ashesh Chattopadhyay; Romit Maulik (CoRR, 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)

Reduced-order modeling on a near-term quantum computer. - Katherine Asztalos; René Steijl; Romit Maulik (J. Comput. Phys., 2024)

Semi-Implicit Neural Ordinary Differential Equations. - Hong Zhang; Ying Liu; Romit Maulik (AAAI, 2025)

Leveraging interpolation models and error bounds for verifiable scientific machine learning. - Tyler H. Chang; Andrew Gillette; Romit Maulik (J. Comput. Phys., 2025)

Binned Spectral Power Loss for Improved Prediction of Chaotic Systems. - Dibyajyoti Chakraborty; Arvind T. Mohan; Romit Maulik (CoRR, 2025)

CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints. - Xin Wang; Juntao Yang; Jeff Adie; Simon See; Kalli Furtado; Chen Chen; Troy Arcomano; Romit Maulik; Gianmarco Mengaldo (CoRR, 2025)

SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning. - Xuyang Li; Romit Maulik (CoRR, 2025)

FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models. - Riddhiman Raut; Romit Maulik; Shivam Barwey (CoRR, 2025)

Multimodal Atmospheric Super-Resolution With Deep Generative Models. - Dibyajyoti Chakraborty; Haiwen Guan; Jason Stock; Troy Arcomano; Guido Cervone; Romit Maulik (CoRR, 2025)