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Panagiotis Stinis

Orcid: 0000-0002-9928-5637

According to our database¹, Panagiotis Stinis authored at least 45 papers between 2004 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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On csauthors.net:

Bibliography

2024

Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations.

[BibT_eX]

[DOI]

,

J. Comput. Phys., February, 2024

SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics.

[BibT_eX]

[DOI]

,

,

Paul J. Atzberger

J. Comput. Phys., 2024

Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks.

[BibT_eX]

[DOI]

,

Amanda A. Howard

,

CoRR, 2024

Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems.

[BibT_eX]

[DOI]

Amanda A. Howard

,

,

Sarah H. Murphy

,

Alexander Heinlein

,

CoRR, 2024

Scientific machine learning for closure models in multiscale problems: a review.

[BibT_eX]

[DOI]

Benjamin Sanderse

,

,

,

CoRR, 2024

Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems.

[BibT_eX]

[DOI]

Alexander Heinlein

,

Amanda A. Howard

,

Damien Beecroft

,

CoRR, 2024

2023

Multifidelity deep operator networks for data-driven and physics-informed problems.

[BibT_eX]

[DOI]

Amanda A. Howard

,

,

George Em Karniadakis

,

J. Comput. Phys., November, 2023

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

[BibT_eX]

[DOI]

,

,

Amanda A. Howard

,

George Em Karniadakis

,

J. Comput. Phys., November, 2023

Stacked networks improve physics-informed training: applications to neural networks and deep operator networks.

[BibT_eX]

[DOI]

Amanda A. Howard

,

Sarah H. Murphy

,

,

CoRR, 2023

Efficient kernel surrogates for neural network-based regression.

[BibT_eX]

[DOI]

,

,

,

,

,

CoRR, 2023

Exploring Learned Representations of Neural Networks with Principal Component Analysis.

[BibT_eX]

[DOI]

,

,

,

CoRR, 2023

Physics-informed machine learning of the correlation functions in bulk fluids.

[BibT_eX]

[DOI]

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,

CoRR, 2023

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

[BibT_eX]

[DOI]

,

,

CoRR, 2023

A multifidelity approach to continual learning for physical systems.

[BibT_eX]

[DOI]

Amanda A. Howard

,

,

CoRR, 2023

A Multifidelity deep operator network approach to closure for multiscale systems.

[BibT_eX]

[DOI]

,

CoRR, 2023

ViTO: Vision Transformer-Operator.

[BibT_eX]

[DOI]

,

,

,

,

George Em Karniadakis

CoRR, 2023

SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics.

[BibT_eX]

[DOI]

,

Constantinos Daskalakis

,

Paul J. Atzberger

CoRR, 2023

2022

SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations.

[BibT_eX]

[DOI]

,

,

George Em Karniadakis

,

CoRR, 2022

Multifidelity Deep Operator Networks.

[BibT_eX]

[DOI]

Amanda A. Howard

,

,

George E. Karniadakis

,

CoRR, 2022

Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery.

[BibT_eX]

[DOI]

,

,

,

Alexandre M. Tartakovsky

CoRR, 2022

Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling.

[BibT_eX]

[DOI]

,

Nathaniel Trask

,

Proceedings of the Mathematical and Scientific Machine Learning, 2022

2021

A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure.

[BibT_eX]

[DOI]

,

David A. Barajas-Solano

,

,

,

Multiscale Model. Simul., 2021

Machine-learning custom-made basis functions for partial differential equations.

[BibT_eX]

[DOI]

,

,

CoRR, 2021

Physics-constrained deep neural network method for estimating parameters in a redox flow battery.

[BibT_eX]

[DOI]

,

,

Alexandre M. Tartakovsky

CoRR, 2021

Time-dependent stochastic basis adaptation for uncertainty quantification.

[BibT_eX]

[DOI]

Ramakrishna Tipireddy

,

,

Alexandre M. Tartakovsky

CoRR, 2021

Optimal renormalization of multi-scale systems.

[BibT_eX]

[DOI]

,

,

Madelyn Shapiro

,

CoRR, 2021

Machine learning structure preserving brackets for forecasting irreversible processes.

[BibT_eX]

[DOI]

,

Nathaniel Trask

,

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Enforcing Constraints for Time Series Prediction in Supervised, Unsupervised and Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 23rd - to, 2020

2019

Renormalized Reduced Order Models with Memory for Long Time Prediction.

[BibT_eX]

[DOI]

,

Multiscale Model. Simul., 2019

Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks.

[BibT_eX]

[DOI]

,

,

Alexandre M. Tartakovsky

,

J. Comput. Phys., 2019

Model reduction for a power grid model.

[BibT_eX]

[DOI]

,

CoRR, 2019

A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations.

[BibT_eX]

[DOI]

Ramakrishna Tipireddy

,

Paris Perdikaris

,

,

Alexandre M. Tartakovsky

CoRR, 2019

2018

Dynamic Looping of a Free-Draining Polymer.

[BibT_eX]

[DOI]

,

,

SIAM J. Appl. Math., 2018

Stochastic Basis Adaptation and Spatial Domain Decomposition for Partial Differential Equations with Random Coefficients.

[BibT_eX]

[DOI]

Ramakrishna Tipireddy

,

,

Alexandre M. Tartakovsky

SIAM/ASA J. Uncertain. Quantification, 2018

Doing the impossible: Why neural networks can be trained at all.

[BibT_eX]

[DOI]

Nathan O. Hodas

,

CoRR, 2018

2017

Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients.

[BibT_eX]

[DOI]

Ramakrishna Tipireddy

,

,

Alexandre M. Tartakovsky

J. Comput. Phys., 2017

Solving differential equations with unknown constitutive relations as recurrent neural networks.

[BibT_eX]

[DOI]

,

,

,

Alexandre M. Tartakovsky

CoRR, 2017

2016

A unified framework for mesh refinement in random and physical space.

[BibT_eX]

[DOI]

,

J. Comput. Phys., 2016

2015

Mesh refinement for uncertainty quantification through model reduction.

[BibT_eX]

[DOI]

,

J. Comput. Phys., 2015

2012

Numerical Computation of Solutions of the Critical Nonlinear Schrödinger Equation after the Singularity.

[BibT_eX]

[DOI]

Multiscale Model. Simul., 2012

Stochastic global optimization as a filtering problem.

[BibT_eX]

[DOI]

J. Comput. Phys., 2012

Improved particle filters for multi-target tracking.

[BibT_eX]

[DOI]

Vasileios Maroulas

,

J. Comput. Phys., 2012

2009

Variance Reduction for Particle Filters of Systems With Time Scale Separation.

[BibT_eX]

[DOI]

,

Panagiotis Stinis

,

IEEE Trans. Signal Process., 2009

2007

Higher Order Mori-Zwanzig Models for the Euler Equations.

[BibT_eX]

[DOI]

Panagiotis Stinis

Multiscale Model. Simul., 2007

2004

Stochastic Optimal Prediction for the Kuramoto-Sivashinsky Equation.

[BibT_eX]

[DOI]

Panagiotis Stinis

Multiscale Model. Simul., 2004

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