303 citations · 2.2k across the 86 of their papers we have counts for
13 papers · 1 filter
Efficient high-dimensional variational data assimilation with machine-learned reduced-order models
Romit Maulik, Vishwas Rao, Jiali Wang +6
Data assimilation (DA) in the geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical we…
Efficient training of artificial neural network surrogates for a collisional-radiative model through adaptive parameter space sampling
Nathan A. Garland, Romit Maulik, Qi Tang +2
Reliable plasma transport modeling for magnetic confinement fusion depends on accurately resolving the ion charge state distribution and radiative power losses of the plasma. These…
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
Romain Egele, Romit Maulik, Krishnan Raghavan +3
Deep neural networks are powerful predictors for a variety of tasks. However, they do not capture uncertainty directly. Using neural network ensembles to quantify uncertainty is co…
Neural-network learning of SPOD latent dynamics
Andrea Lario, Romit Maulik, Oliver T. Schmidt +2
We aim to reconstruct the latent space dynamics of high dimensional, quasi-stationary systems using model order reduction via the spectral proper orthogonal decomposition (SPOD). T…
Machine-learning accelerated turbulence modelling of transient flashing jets
David Schmidt, Romit Maulik, Konstantinos G. Lyras
Modelling the sudden depressurisation of superheated liquids through nozzles is a challenge because the pressure drop causes rapid flash boiling of the liquid. The resulting jet us…
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. Valerio Iungo, Romit Maulik, S. Ashwin Renganathan +1
Wind turbine wakes are the result of the extraction of kinetic energy from the incoming atmospheric wind exerted from a wind turbine rotor. Therefore, the reduced mean velocity and…
Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
Masaki Morimoto, Kai Fukami, Romit Maulik +2
We use Gaussian stochastic weight averaging (SWAG) to assess the model-form uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG ap…
Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning
S. Ashwin Renganathan, Romit Maulik, Stefano Letizia +1
Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions, and the interaction between wakes. Physics-based models tha…
PyParSVD: A streaming, distributed and randomized singular-value-decomposition library
Romit Maulik, Gianmarco Mengaldo
We introduce PyParSVD\footnote{https://github.com/Romit-Maulik/PyParSVD}, a Python library that implements a streaming, distributed and randomized algorithm for the singular value…
Learning the temporal evolution of multivariate densities via normalizing flows
Yubin Lu, Romit Maulik, Ting Gao +3
In this work, we propose a method to learn multivariate probability distributions using sample path data from stochastic differential equations. Specifically, we consider temporall…
Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methods
Boumediene Hamzi, Romit Maulik, Houman Owhadi
Modeling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems. We s…
PythonFOAM: In-situ data analyses with OpenFOAM and Python
Romit Maulik, Dimitrios Fytanidis, Bethany Lusch +2
We outline the development of a general-purpose Python-based data analysis tool for OpenFOAM. Our implementation relies on the construction of OpenFOAM applications that have bindi…
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
Kai Fukami, Romit Maulik, Nesar Ramachandra +2
Achieving accurate and robust global situational awareness of a complex time-evolving field from a limited number of sensors has been a longstanding challenge. This reconstruction…