Romit Maulik | DeepAI

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Gianluigi Rozza
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Ioannis G. Kevrekidis
42 publications
Jinqiao Duan
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Ricardo Vinuesa
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Felix Dietrich
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Indranil Pan
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Yunan Yang
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Ting Gao
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∙ 09/21/2023

Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package

We propose a parallel (distributed) version of the spectral proper ortho...

0 Marcin Rogowski, et al. ∙

∙ 09/13/2023

Generalizable improvement of the Spalart-Allmaras model through assimilation of experimental data

This study focuses on the use of model and data fusion for improving the...

0 Deepinder Jot Singh Aulakh, et al. ∙

∙ 07/25/2023

Differentiable Turbulence II

Differentiable fluid simulators are increasingly demonstrating value as ...

0 Varun Shankar, et al. ∙

∙ 07/07/2023

Differentiable Turbulence

Deep learning is increasingly becoming a promising pathway to improving ...

0 Varun Shankar, et al. ∙

∙ 04/19/2023

Generative Modeling of Time-Dependent Densities via Optimal Transport and Projection Pursuit

Motivated by the computational difficulties incurred by popular deep lea...

0 Jonah Botvinick-Greenhouse, et al. ∙

∙ 02/20/2023

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

Classical problems in computational physics such as data-driven forecast...

0 Romit Maulik, et al. ∙

∙ 02/13/2023

Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learning

The goal of this work is to address two limitations in autoencoder-based...

0 Shivam Barwey, et al. ∙

∙ 09/23/2022

Differentiable physics-enabled closure modeling for Burgers' turbulence

Data-driven turbulence modeling is experiencing a surge in interest foll...

0 Varun Shankar, et al. ∙

∙ 02/22/2022

Multi-fidelity reinforcement learning framework for shape optimization

Deep reinforcement learning (DRL) is a promising outer-loop intelligence...

8 Sahil Bhola, et al. ∙

∙ 10/26/2021

AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification

Deep neural networks are powerful predictors for a variety of tasks. How...

0 Romain Egele, et al. ∙

∙ 10/18/2021

Neural-network learning of SPOD latent dynamics

We aim to reconstruct the latent space dynamics of high dimensional syst...

0 Andrea Lario, et al. ∙

∙ 09/16/2021

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

We use Gaussian stochastic weight averaging (SWAG) to assess the model-f...

9 Masaki Morimoto, et al. ∙

∙ 09/06/2021

Data-Driven Wind Turbine Wake Modeling via Probabilistic Machine Learning

Wind farm design primarily depends on the variability of the wind turbin...

37 S. Ashwin Renganathan, et al. ∙

∙ 08/19/2021

PyParSVD: A streaming, distributed and randomized singular-value-decomposition library

We introduce PyParSVD[https://github.com/Romit-Maulik/PyParSVD], a Pytho...

0 Romit Maulik, et al. ∙

∙ 07/29/2021

Learning the temporal evolution of multivariate densities via normalizing flows

In this work, we propose a method to learn probability distributions usi...

5 Yubin Lu, et al. ∙

∙ 03/17/2021

PythonFOAM: In-situ data analyses with OpenFOAM and Python

In this article, we outline the development of a general-purpose Python-...

0 Romit Maulik, et al. ∙

∙ 01/03/2021

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

Achieving accurate and robust global situational awareness of a complex ...

7 Kai Fukami, et al. ∙

∙ 07/23/2020

Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation

Non-intrusive reduced-order models (ROMs) have recently generated consid...

0 Romit Maulik, et al. ∙

∙ 09/18/2019

Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models

Rapid simulations of advection-dominated problems are vital for multiple...

0 Romit Maulik, et al. ∙

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