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Critical evaluation of Wind Turbines’ analytical wake models

Renewable & sustainable energy reviews, 2021-07, Vol.144, p.110991, Article 110991

2021

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Optimal prediction and the rate of decay for solutions of the Euler equations in two and three dimensions

Proceedings of the National Academy of Sciences - PNAS, 2007-04, Vol.104 (16), p.6527-6532

2007

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Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model

Journal of power sources, 2023-11, Vol.584, p.233548, Article 233548

2023

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Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks

Journal of computational physics, 2019-11, Vol.397, p.108844, Article 108844

2019

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A hybrid deep neural operator/finite element method for ice-sheet modeling

Journal of computational physics, 2023-11, Vol.492, p.112428, Article 112428

2023

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Doing the Impossible: Why Neural Networks Can Be Trained at All

Frontiers in psychology, 2018-07, Vol.9, p.1185-1185

2018

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Higher Order Mori–Zwanzig Models for the Euler Equations

Multiscale modeling & simulation, 2007-01, Vol.6 (3), p.741-760

Stinis, Panagiotis

2007

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A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness

Computer methods in applied mechanics and engineering, 2019-06, Vol.350, p.199-227

2019

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Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding

Frontiers in neuroscience, 2024-02, Vol.18 (_), p.1346805-1346805

2024

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A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness

Computer methods in applied mechanics and engineering, 2019-06, Vol.350 (C)

2019

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Dimensional reduction as a tool for mesh refinement and trackingsingularities of PDEs

Journal of computational physics, 2007-06

Stinis, Panagiotis

2007

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A comparative study of two stochastic mode reduction methods

Physica. D, 2006-01, Vol.213 (2), p.197-213

Stinis, Panagiotis

2006

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A maximum likelihood algorithm for the estimation and renormalization of exponential densities

Journal of computational physics, 2005-09, Vol.208 (2), p.691-703

Stinis, Panagiotis

2005

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Problem reduction, renormalization, and memory

Communications in applied mathematics and computational science, 2006-12, Vol.1 (1), p.1-27

2006

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A phase transition approach to detecting singularities of partial differential equations

Communications in applied mathematics and computational science, 2009-01, Vol.4 (1), p.217-239

Stinis, Panagiotis

2009

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Improving Time Step Convergence in an Atmosphere Model With Simplified Physics: The Impacts of Closure Assumption and Process Coupling

Journal of advances in modeling earth systems, 2020-10, Vol.12 (10), p.n/a

2020

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A maximum likelihood algorithm for the estimation andrenormalization of exponential densities

Journal of computational physics, 2004-08, Vol.208 (2)

Stinis, Panagiotis

2004

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Variance Reduction for Particle Filters of Systems With Time Scale Separation

IEEE transactions on signal processing, 2009-02, Vol.57 (2), p.424-435

2009

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Stochastic Optimal Prediction for the Kuramoto--Sivashinsky Equation

Multiscale modeling & simulation, 2004-01, Vol.2 (4), p.580-612

Stinis, Panagiotis

2004

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Optimal prediction for the Kuramoto -Sivashinsky equation

Stinis, Panagiotis

2003

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Critical evaluation of Wind Turbines’ analytical wake models

Optimal prediction and the rate of decay for solutions of the Euler equations in two and three dimensions

Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model

Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks

A hybrid deep neural operator/finite element method for ice-sheet modeling

Doing the Impossible: Why Neural Networks Can Be Trained at All

Higher Order Mori–Zwanzig Models for the Euler Equations

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness

Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness

Dimensional reduction as a tool for mesh refinement and trackingsingularities of PDEs

A comparative study of two stochastic mode reduction methods

A maximum likelihood algorithm for the estimation and renormalization of exponential densities

Problem reduction, renormalization, and memory

A phase transition approach to detecting singularities of partial differential equations

Improving Time Step Convergence in an Atmosphere Model With Simplified Physics: The Impacts of Closure Assumption and Process Coupling

A maximum likelihood algorithm for the estimation andrenormalization of exponential densities

Variance Reduction for Particle Filters of Systems With Time Scale Separation

Stochastic Optimal Prediction for the Kuramoto--Sivashinsky Equation

Optimal prediction for the Kuramoto -Sivashinsky equation

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