Panagiotis Stinis

According to our database, Panagiotis Stinis authored at least 42 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

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

Wenqian Chen, Panos Stinis

J. Comput. Phys., February, 2024

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

Benjamin Sanderse, Panos Stinis, Romit Maulik, Shady E. Ahmed

CoRR, 2024

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

Alexander Heinlein, Amanda A. Howard, Damien Beecroft, Panos Stinis

CoRR, 2024

2023

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

Amanda A. Howard, Mauro Perego, George Em Karniadakis, Panos Stinis

J. Comput. Phys., November, 2023

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

QiZhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis

J. Comput. Phys., November, 2023

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

Amanda A. Howard, Sarah H. Murphy, Shady E. Ahmed, Panos Stinis

CoRR, 2023

Efficient kernel surrogates for neural network-based regression.

Saad Qadeer, Andrew Engel, Adam Tsou, Max Vargas, Panos Stinis, Tony Chiang

CoRR, 2023

Exploring Learned Representations of Neural Networks with Principal Component Analysis.

Amit Harlev, Andrew Engel, Panos Stinis, Tony Chiang

CoRR, 2023

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

Wenqian Chen, Peiyuan Gao, Panos Stinis

CoRR, 2023

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

Wenqian Chen, Yucheng Fu, Panos Stinis

CoRR, 2023

A multifidelity approach to continual learning for physical systems.

Amanda A. Howard, Yucheng Fu, Panos Stinis

CoRR, 2023

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

Shady E. Ahmed, Panos Stinis

CoRR, 2023

ViTO: Vision Transformer-Operator.

Oded Ovadia, Adar Kahana, Panos Stinis, Eli Turkel, George Em Karniadakis

CoRR, 2023

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

Panos Stinis, Constantinos Daskalakis, Paul J. Atzberger

CoRR, 2023

2022

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

Qian Zhang, Adar Kahana, George Em Karniadakis, Panos Stinis

CoRR, 2022

Multifidelity Deep Operator Networks.

Amanda A. Howard, Mauro Perego, George E. Karniadakis, Panos Stinis

CoRR, 2022

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

QiZhi He, Yucheng Fu, Panos Stinis, Alexandre M. Tartakovsky

CoRR, 2022

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

Kookjin Lee, Nathaniel Trask, Panos Stinis

Proceedings of the Mathematical and Scientific Machine Learning, 2022

2021

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

Jacob Roth, David A. Barajas-Solano, Panos Stinis, Jonathan Weare, Mihai Anitescu

Multiscale Model. Simul., 2021

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

Brek Meuris, Saad Qadeer, Panos Stinis

CoRR, 2021

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

QiZhi He, Panos Stinis, Alexandre M. Tartakovsky

CoRR, 2021

Time-dependent stochastic basis adaptation for uncertainty quantification.

Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky

CoRR, 2021

Optimal renormalization of multi-scale systems.

Jacob Price, Brek Meuris, Madelyn Shapiro, Panos Stinis

CoRR, 2021

Machine learning structure preserving brackets for forecasting irreversible processes.

Kookjin Lee, Nathaniel Trask, Panos Stinis

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.

Panos Stinis

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.

Jacob Price, Panos Stinis

Multiscale Model. Simul., 2019

Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks.

Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung

J. Comput. Phys., 2019

Model reduction for a power grid model.

Jing Li, Panos Stinis

CoRR, 2019

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

Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre M. Tartakovsky

CoRR, 2019

2018

Dynamic Looping of a Free-Draining Polymer.

Felix X.-F. Ye, Panos Stinis, Hong Qian

SIAM J. Appl. Math., 2018

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

Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky

SIAM/ASA J. Uncertain. Quantification, 2018

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

Nathan O. Hodas, Panos Stinis

CoRR, 2018

2017

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

Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky

J. Comput. Phys., 2017

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

Tobias Hagge, Panos Stinis, Enoch Yeung, Alexandre M. Tartakovsky

CoRR, 2017

2016

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

Jing Li, Panos Stinis

J. Comput. Phys., 2016

2015

Mesh refinement for uncertainty quantification through model reduction.

Jing Li, Panos Stinis

J. Comput. Phys., 2015

2012

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

Panos Stinis

Multiscale Model. Simul., 2012

Stochastic global optimization as a filtering problem.

Panos Stinis

J. Comput. Phys., 2012

Improved particle filters for multi-target tracking.

Vasileios Maroulas, Panos Stinis

J. Comput. Phys., 2012

2009

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

Dror Givon, Panagiotis Stinis, Jonathan Weare

IEEE Trans. Signal Process., 2009

2007

Higher Order Mori-Zwanzig Models for the Euler Equations.

Panagiotis Stinis

Multiscale Model. Simul., 2007

2004

Stochastic Optimal Prediction for the Kuramoto-Sivashinsky Equation.

Panagiotis Stinis

Multiscale Model. Simul., 2004