- Modeling Wind Turbine Performance and Wake Interactions with Machine Learning, C. Moss, R. Maulik, G. V. Iungo.
- Predicting Wind Farm Operations with Machine Learning and the P2D-RANS model: A Case Study for an AWAKEN Site, C. Moss, M. Puccioni, R. Maulik, C. Jacquet, D. Apgar, G. V. Iungo.
- Multi-fidelity reinforcement learning framework for shape optimization, S. Bhola, S. Pawar, P. Balaprakash, R. Maulik.
- A physics-consistent machine learning model with uncertainty propagation to predict spatiotemporal boundary conditions for coupled simulations, S. Mondal, G. Magnotti, B. Lusch, R. Maulik, R. Torelli.
- Physics-Informed Neural Networks for Mesh Deformation with Exact Boundary Enforcement, A Aygun, R. Maulik, A. Karakus.

- Differentiable physics-enabled closure modeling for Burgers' turbulence, V. Shankar, V. Puri, R. Balakrishnan, V. Vishwanathan, Machine Learning Science and Technology (Accepted 2023).
- Machine Learning-Enabled Prediction of Transient Injection Map in Automotive Injectors With Uncertainty Quantification, , S. Mondal, G. Magnotti, B. Lusch, R. Maulik, R. Torelli, Journal of Engineering for Gas Turbines and Power, 145 (4), 041015, 2023.
- Politics of Problem Definition: Comparing Public Support of Climate Change Mitigation Policies using Machine Learning, J. Choi, W. Wehde, R. Maulik., Review of Policy Research (Accepted), 2022.
- Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems, A. Linot, J. Burby, Q. Tang, P. Balaprakash, M. Graham, R. Maulik, Journal of Computational Physics, 474, 111838, 2022.
- Efficient training of artificial neural network surrogates for a collisional-radiative model through adaptive parameter space sampling, N. Garland, R. Maulik, Q. Tang, X. Tang, P. Balaprakash, Machine Learning: Science and Technology 3 (4), 045003, 2022.
- Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression, M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa, K. Fukagata, Physica D., 133454, 2022.
- Neural-network learning of SPOD latent dynamics, A. Lario, R. Maulik, G. Rozza, G. Mengaldo, Journal of Computational Physics, 111475, 2022.
- Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements, G. Iungo, R. Maulik, S. Renganathan, S. Letizia. Journal of Renewable and Sustainable Energy, 14 (Cover article), 023307 (2022).
- Efficient high-dimensional variational data assimilation with machine-learned reduced-order models, R. Maulik, V. Rao, J. Wang, G. Mengaldo, E. Constantinescu, B. Lusch, P. Balaprakash, I. Foster, R. Kotamarthi. Geoscientific Model Development, 15, 3433–3445, 2022.
- Learning the temporal evolution of multivariate densities via normalizing flows, Y. Lu, R. Maulik, T. Gao, F. Dietrich, I. Kevrekidis, J. Duan. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32 (3), 033121, 2022.
- PythonFOAM: In-situ data analyses with OpenFOAM and Python, R. Maulik, D. Fytanidis, B. Lusch, V, Vishwanath, S. Patel. Journal of Computational Science, 62, 101750, 2022.
- Data-driven wind turbine wake modeling using probabilistic machine learning, S. Renganathan, R. Maulik, G. Iungo, S. Letizia. Neural Computing and Applications, 34, 6171–6186, 2022.
- Machine-learning accelerated turbulence modelling of transient flashing jets, K. Lyras, R. Maulik, D. Schmidt. Physics of Fluids, 33, 127104 (2021).
- Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning, K. Fukami, R. Maulik, N. Ramachandra, K. Taira, K. Fukagata. Nature Machine Intelligence, 2021.
- Simple, low-cost, and accurate, data-driven geophysical forecasting with learned kernels, B. Hamzi, R. Maulik, H. Owhadi. Proceedings of the Royal Society A, 477: 20210326, 2021.
- PySPOD: A Python package for Spectral Proper Orthogonal Decomposition (SPOD), G. Mengaldo, R. Maulik, Journal of Open Source Software, 6 (60), 2862, 2021.
- Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders, R. Maulik, B. Lusch, P. Balaprakash, Physics of Fluids, 33, 037106, 2021.
- Distributed deep reinforcement learning for simulation control, S. Pawar, R. Maulik, Machine Learning: Science and Technology, 2, 025029, 2021.
- Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization, S. Renganathan, R. Maulik, J. Ahuja, Aerospace Science and Technology, 111, 106522, 2021.
- Fast neural Poincaré maps for toroidal magnetic fields, J. Burby, Q. Tang, R. Maulik, Plasma Physics and Controlled Fusion, 63 (2), 024001, 2020.
- Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation, R. Maulik, T. Botsas, N. Ramachandra, L. Mason, I. Pan, Physica D, 132797, 2020.
- A turbulent eddy-viscosity surrogate modeling framework for Reynolds-Averaged Navier-Stokes simulations, R. Maulik, H. Sharma, S. Patel, B. Lusch, E. Jennings, Computers and Fluids, 104777, 2020.
- Probabilistic neural networks for fluid flow model-order reduction and data recovery, R. Maulik, K. Fukami, N. Ramachandra, K. Taira, K. Fukagata, Physical Review Fluids, 5, 104401, 2020.
- Non-autoregressive time-series methods for stable parameteric reduced-order models, R. Maulik, B. Lusch, P. Balaprakash, Physics of Fluids, 32, 087115 (2020).
- Neural network representability of fully ionized plasma fluid model closures, R. Maulik, N. Garland, J. Burby, X. Tang, P. Balaprakash, Physics of Plasmas 27, 072106, 2020.
- What Matters the Most for Individual Disaster Preparedness? Understanding Emergency Preparedness Using Machine Learning, J. Choi, S. Robinson, R. Maulik, W. Wehde, Natural Hazards, 2020
- Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil, S. Renganathan, R. Maulik, V. Rao, Physics of Fluids, 32, 047110, 2020
- Numerical assessments of a parametric implicit large eddy simulation model, R. Maulik, O. San, Journal of Computational and Applied Mathematics, 112866, 2020
- Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers, R. Maulik, O. San, J. Jacob, Physica D., 406, 132409, 2020
- Time-series learning of latent-space dynamics for reduced-order model closure, R. Maulik, A. Mohan, B. Lusch, S. Madireddy, P. Balaprakash, D. Livescu, Physica D., 405, 132368, 2020
- Improvement of Unitary Equipment and Heat Exchanger Testing Methods, Y. Hossain, R. Maulik, H. Park, M. Ahmed, C. Bach, O. San, ASHRAE Transactions, 125(2), 2019.
- Online turbulence model classification for large eddy simulation using deep learning, R. Maulik, O. San, J. Jacob, C. Crick, Journal of Fluid Mechanics, 870 (784-812), 2019
- An artificial neural network framework for reduced order modeling of transient flows, O.San, R. Maulik, M. Ahmed, Communications in Nonlinear Science and Numerical Simulation, 77 (271-287), 2019
- Subgrid modeling for two-dimensional turbulence using artificial neural networks, R. Maulik, O. San, A. Rasheed, P. Vedula, Journal of Fluid Mechanics, 858 (122-144), 2019
- Data-driven deconvolution for large eddy simulation of Kraichnan turbulence, R. Maulik, O. San, A. Rasheed, P. Vedula, Physics of Fluids, 30, 125109, 2018
- Stratified Kelvin-Helmholtz turbulence of compressible shear flows, O. San, R. Maulik, Nonlinear Processes in Geophysics, 25, (457-476), 2018
- Extreme learning machine for reduced order modeling of turbulent geophysical flows, O. San, R. Maulik, Physical Review E, 97, 042322, 2018
- Machine learning closures for model order reduction of thermal fluids, O. San, R. Maulik, Applied Mathematical Modelling, 2018
- An adaptive multilevel wavelet framework for scale-selective WENO reconstruction schemes, R. Maulik, O. San, R. Behera, International Journal of Numerical Methods in Fluids, 2018
- A dynamic closure modeling framework for large eddy simulation using approximate deconvolution, R. Maulik, O. San, Cogent Physics, 5, 2018
- Neural network closure models for nonlinear model order reduction, O. San, R. Maulik, Advances in Computational Mathematics, 2018
- A neural network approach for the blind deconvolution of turbulent flows, R. Maulik, O. San, Journal of Fluid Mechanics, 831 (151-181), 2017
- A novel dynamic framework for subgrid-scale parametrization of mesoscale eddies in quasigeostrophic turbulent flows, R. Maulik, O. San, Computer and Mathematics with Applications, 74 (420-445), 2017
- Explicit and implicit LES closures for Burgers turbulence, R. Maulik, O. San, Journal of Computational and Applied Mathematics, 327 (12-40), 2017
- Resolution and energy dissipation characteristics of implicit LES and explicit filtering models for compressible turbulence , R. Maulik, O. San, Fluids, 2, 2017
- A dynamic subgrid-scale modeling framework for Boussinesq turbulence , R. Maulik, O. San, International Journal of Heat and Mass Transfer, 108 (1656-1675), 2017
- A dynamic framework for scale-aware parameterizations of eddy viscosity coefficient in two-dimensional turbulence , R. Maulik, O. San, International Journal of Computational Fluid Dynamics, 31 (69-92), 2017
- A stable and scale-aware dynamic modeling framework for subgrid-scale parameterizations of two-dimensional turbulence , R. Maulik, O. San, Computers and Fluids, 158 (11-38), 2016
- Dynamic modeling of the horizontal eddy viscosity coefficient for quasigeostrophic ocean circulation problems , R. Maulik, O. San, Journal of Ocean Engineering and Science, 1 (300-324), 2016
- A biphasic transversely isotropic poroviscoelastic model for the unconfined compression of hydrated soft tissue , H. Hatami, R. Maulik, Journal of Biomechanical Engineering, 138, 031003, 2016

- AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification, R. Egele, R. Maulik, K. Raghavan, P. Balaprakash, B. Lusch, International Conference on Pattern Recognition (ICPR), 2022.
- S. Mondal, G. Magnotti, B. Lusch, R. Maulik, R. Torelli: Machine Learning-Enabled Prediction of Transient Injection Map in Automotive Injectors With Uncertainty Quantification, ASME Internal Combustion Engine Fall Conference, 2021.
- Data-driven deep learning emulators for geophysical forecasting, V. Rao, R. Maulik, V. Rao, B. Lusch, S. Renganathan, R. Kotamarthi, International Conference on Computational Science, 433-446, 2021
- Cluster analysis of wind turbine wakes measured through a scanning Doppler wind LiDAR, R. Maulik, V. Rao, S. Renganathan, S. Letizia, G. Iungo, AIAA SciTech Forum, 2021
- Deploying deep learning in OpenFOAM with TensorFlow, R. Maulik, H. Sharma, S. Patel, B. Lusch, E. Jennings, AIAA SciTech Forum, 2021
- Data-Driven Modeling of Large-Eddy Simulations for Fuel Injector Design, P. Milan, R. Torelli, B. Lusch, R. Maulik, G. Magnotti, AIAA SciTech Forum, 2021
- Application of Artificial Neural Network in the APS LINAC Bunch Charge Transmission Efficiency, H. Shang , Y. Sun, R. Maulik, T. Xu, 12th International Particle Accelerator Conference (IPAC), 2021.
- Recurrent neural network architecture search for geophysical emulation, R. Maulik, R. Egele, B. Lusch, P. Balaprakash, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2020.
- A Machine Learning Method for Computing Rare Event Probabilities, V. Rao, R. Maulik, E. Constantinescu, M. Anitescu, International Conference on Computational Science, 169-182, 2020
- A computational investigation of the effect of ground clearance in vertical ducting systems - ASHRAE RP-1743, R. Maulik, O. San, C. Bach, International High Performance Buildings Conference, Herrick Laboratories, Purdue University, 2018

- Cluster analysis of wind turbine wakes measured through a scanning Doppler wind LiDAR, R. Maulik, V. Rao, S. Renganathan, S. Letizia, G. Iungo, AIAA SciTech Forum, 2021
- Deploying deep learning in OpenFOAM with TensorFlow, R. Maulik, H. Sharma, S. Patel, B. Lusch, E. Jennings, AIAA SciTech Forum, 2021
- Data-Driven Modeling of Large-Eddy Simulations for Fuel Injector Design, P. Milan, R. Torelli, B. Lusch, R. Maulik, G. Magnotti, AIAA SciTech Forum, 2021

- PyParSVD: A streaming, distributed and randomized singular-value-decomposition library, R. Maulik, G. Mengaldo, 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7), Supercomputing 2021.
- Meta-modeling strategy for data-driven forecasting, R. Maulik, D. Skinner, Tackling Climate Change with Machine Learning Workshop - NeurIPS 2020
- Progress towards high fidelity collisional-radiative model surrogates for rapid in-situ evaluation, N. Garland, R. Maulik, Q. Tang, X. Tang, P. Balaprakash, Machine Learning for Physical Sciences Workshop - NeurIPS 2020
- Probabilistic neural network-based reduced-order surrogate for fluid flows, K. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, K. Taira, Machine Learning for Physical Sciences Workshop - NeurIPS 2020
- Site-specific graph neural network for predicting protonation energy of oxygenate molecules, R. Maulik, R. Assary, P. Balaprakash, Machine Learning for Physical Sciences Workshop - NeurIPS 2019
- Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models, R. Maulik, V. Rao, S. Madireddy, B. Lusch, P. Balaprakash, Machine Learning for Physical Sciences Workshop - NeurIPS 2019