Romit Maulik

Assistant Professr

Pennsylvania State University

United States

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Romit Maulik is an Assistant Professor at Pennsylvania State University, USA. His research focuses on scientific machine learning, dynamical systems, and data assimilation, as reflected in recent publications exploring topics such as neural ordinary differential equations, turbulence modeling, and machine learning applications in climate policy. He is dedicated to advancing understanding in multiscale problems and improving data-driven approaches in engineering and environmental sciences.

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2025

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AUSTRALIA

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S. Letizia

UNITED STATES

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Kwanjung Yee

Seoul National University

SOUTH KOREA

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Namwoo Kang

Assistant Professor

Korea Advanced Institute of Science and Technology

SOUTH KOREA

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Venkatasubramanian Viswanathan

Associate Professor

Carnegie Mellon University

UNITED STATES

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Romit Maulik

Research Interests

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About

Articles (20)

Understanding latent timescales in neural ordinary differential equation models of advection-dominated dynamical systems

Leveraging interpolation models and error bounds for verifiable scientific machine learning

Differentiable turbulence: Closure as a partial differential equation constrained optimization

Scientific machine learning for closure models in multiscale problems: A review

Augmenting insights from wind turbine data through data-driven approaches

Data-driven physics-informed neural networks: A digital twin perspective

Robust experimental data assimilation for the Spalart-Allmaras turbulence model

Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

Politics of problem definition: Comparing public support of climate change mitigation policies using machine learning

Reduced-order modeling on a near-term quantum computer

Differentiable physics-enabled closure modeling for Burgers’ turbulence

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

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

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

Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

PythonFOAM: In-situ data analyses with OpenFOAM and Python

Learning the temporal evolution of multivariate densities via normalizing flows

Machine learning accelerated turbulence modeling of transient flashing jets

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

Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels

Collaborators (9)

David Schmidt

Qi Tang

S. Ashwin Renganathan

Giacomo Valerio Iungo

Nathan Garland

S. Letizia

Kwanjung Yee

Namwoo Kang

Venkatasubramanian Viswanathan

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