Publications

Under-review
  1. Modeling Wind Turbine Performance and Wake Interactions with Machine Learning, C. Moss, R. Maulik, G. V. Iungo.
  2. 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.
  3. Multi-fidelity reinforcement learning framework for shape optimization, S. Bhola, S. Pawar, P. Balaprakash, R. Maulik.
  4. 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.
  5. Physics-Informed Neural Networks for Mesh Deformation with Exact Boundary Enforcement, A Aygun, R. Maulik, A. Karakus.
Peer-reviewed journal articles
  1. Differentiable physics-enabled closure modeling for Burgers' turbulence, V. Shankar, V. Puri, R. Balakrishnan, V. Vishwanathan, Machine Learning Science and Technology (Accepted 2023).
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. Neural-network learning of SPOD latent dynamics, A. Lario, R. Maulik, G. Rozza, G. Mengaldo, Journal of Computational Physics, 111475, 2022.
  8. 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).
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. Machine-learning accelerated turbulence modelling of transient flashing jets, K. Lyras, R. Maulik, D. Schmidt. Physics of Fluids, 33, 127104 (2021).
  14. 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.
  15. 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.
  16. PySPOD: A Python package for Spectral Proper Orthogonal Decomposition (SPOD), G. Mengaldo, R. Maulik, Journal of Open Source Software, 6 (60), 2862, 2021.
  17. 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.
  18. Distributed deep reinforcement learning for simulation control, S. Pawar, R. Maulik, Machine Learning: Science and Technology, 2, 025029, 2021.
  19. 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.
  20. Fast neural Poincaré maps for toroidal magnetic fields, J. Burby, Q. Tang, R. Maulik, Plasma Physics and Controlled Fusion, 63 (2), 024001, 2020.
  21. 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.
  22. 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.
  23. 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.
  24. Non-autoregressive time-series methods for stable parameteric reduced-order models, R. Maulik, B. Lusch, P. Balaprakash, Physics of Fluids, 32, 087115 (2020).
  25. 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.
  26. 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
  27. 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
  28. Numerical assessments of a parametric implicit large eddy simulation model, R. Maulik, O. San, Journal of Computational and Applied Mathematics, 112866, 2020
  29. Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers, R. Maulik, O. San, J. Jacob, Physica D., 406, 132409, 2020
  30. 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
  31. 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.
  32. 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
  33. 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
  34. 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
  35. Data-driven deconvolution for large eddy simulation of Kraichnan turbulence, R. Maulik, O. San, A. Rasheed, P. Vedula, Physics of Fluids, 30, 125109, 2018
  36. Stratified Kelvin-Helmholtz turbulence of compressible shear flows, O. San, R. Maulik, Nonlinear Processes in Geophysics, 25, (457-476), 2018
  37. Extreme learning machine for reduced order modeling of turbulent geophysical flows, O. San, R. Maulik, Physical Review E, 97, 042322, 2018
  38. Machine learning closures for model order reduction of thermal fluids, O. San, R. Maulik, Applied Mathematical Modelling, 2018
  39. 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
  40. A dynamic closure modeling framework for large eddy simulation using approximate deconvolution, R. Maulik, O. San, Cogent Physics, 5, 2018
  41. Neural network closure models for nonlinear model order reduction, O. San, R. Maulik, Advances in Computational Mathematics, 2018
  42. A neural network approach for the blind deconvolution of turbulent flows, R. Maulik, O. San, Journal of Fluid Mechanics, 831 (151-181), 2017
  43. 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
  44. Explicit and implicit LES closures for Burgers turbulence, R. Maulik, O. San, Journal of Computational and Applied Mathematics, 327 (12-40), 2017
  45. Resolution and energy dissipation characteristics of implicit LES and explicit filtering models for compressible turbulence , R. Maulik, O. San, Fluids, 2, 2017
  46. A dynamic subgrid-scale modeling framework for Boussinesq turbulence , R. Maulik, O. San, International Journal of Heat and Mass Transfer, 108 (1656-1675), 2017
  47. 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
  48. 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
  49. 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
  50. 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
Peer-reviewed conference proceedings
  1. AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification, R. Egele, R. Maulik, K. Raghavan, P. Balaprakash, B. Lusch, International Conference on Pattern Recognition (ICPR), 2022.
  2. 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.
  3. 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
  4. 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
  5. Deploying deep learning in OpenFOAM with TensorFlow, R. Maulik, H. Sharma, S. Patel, B. Lusch, E. Jennings, AIAA SciTech Forum, 2021
  6. 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
  7. 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.
  8. 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.
  9. 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
  10. 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
Conference proceedings
  1. 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
  2. Deploying deep learning in OpenFOAM with TensorFlow, R. Maulik, H. Sharma, S. Patel, B. Lusch, E. Jennings, AIAA SciTech Forum, 2021
  3. 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
Peer-reviewed workshop proceedings
  1. 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.
  2. Meta-modeling strategy for data-driven forecasting, R. Maulik, D. Skinner, Tackling Climate Change with Machine Learning Workshop - NeurIPS 2020
  3. 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
  4. 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
  5. 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
  6. 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
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