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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...
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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...
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07/25/2023
Differentiable Turbulence II
Differentiable fluid simulators are increasingly demonstrating value as ...
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07/07/2023
Differentiable Turbulence
Deep learning is increasingly becoming a promising pathway to improving ...
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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...
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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...
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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...
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09/23/2022
Differentiable physics-enabled closure modeling for Burgers' turbulence
Data-driven turbulence modeling is experiencing a surge in interest foll...
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02/22/2022
Multi-fidelity reinforcement learning framework for shape optimization
Deep reinforcement learning (DRL) is a promising outer-loop intelligence...
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10/26/2021
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
Deep neural networks are powerful predictors for a variety of tasks. How...
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10/18/2021
Neural-network learning of SPOD latent dynamics
We aim to reconstruct the latent space dynamics of high dimensional syst...
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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...
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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...
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08/19/2021
PyParSVD: A streaming, distributed and randomized singular-value-decomposition library
We introduce PyParSVD[https://github.com/Romit-Maulik/PyParSVD], a Pytho...
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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...
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03/17/2021
PythonFOAM: In-situ data analyses with OpenFOAM and Python
In this article, we outline the development of a general-purpose Python-...
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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 ...
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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...
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09/18/2019
