Panos Stinis

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All published works

Action	Title	Year	Authors
+ PDF Chat	What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications	2024	Emily Williams Amanda A. Howard Brek Meuris Panos Stinis
+ PDF Chat	SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks	2024	Bruno Jacob Amanda A. Howard Panos Stinis
+ PDF Chat	Multifidelity Kolmogorov-Arnold Networks	2024	Amanda A. Howard Bruno Jacob Panos Stinis
+ PDF Chat	SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations	2024	Qian Zhang Adar Kahana George Em Karniadakis Panos Stinis
+ PDF Chat	Multiscale modeling framework of a constrained fluid with complex boundaries using twin neural networks	2024	Peiyuan Gao George Em Karniadakis Panos Stinis
+ PDF Chat	Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems	2024	Amanda A. Howard Bruno Jacob Sarah H. Murphy Alexander Heinlein Panos Stinis
+ PDF Chat	Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks	2024	Wenqian Chen Amanda A. Howard Panos Stinis
+ PDF Chat	ViTO: Vision Transformer-Operator	2024	Oded Ovadia Adar Kahana Panos Stinis Eli Turkel Dan Givoli George Em Karniadakis
+ PDF Chat	Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance	2024	Yucheng Fu Amanda A. Howard Chao Zeng Yunxiang Chen Peiyuan Gao Panos Stinis
+ PDF Chat	A multifidelity approach to continual learning for physical systems	2024	Amanda A. Howard Yucheng Fu Panos Stinis
+ PDF Chat	Scientific machine learning for closure models in multiscale problems: a review	2024	Benjamin Sanderse Panos Stinis Romit Maulik Shady E. Ahmed
+ PDF Chat	Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding	2024	Youngeun Kim Adar Kahana Ruokai Yin Yuhang Li Panos Stinis George Em Karniadakis Priyadarshini Panda
+	Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems	2024	Alexander Heinlein Amanda A. Howard Damien Beecroft Panos Stinis
+ PDF Chat	Physics-informed machine learning of the correlation functions in bulk fluids	2024	Wenqian Chen Peiyuan Gao Panos Stinis
+	Stacked networks improve physics-informed training: Applications to neural networks and deep operator networks	2024	Amanda A. Howard Sarah H. Murphy Shady E. Ahmed Panos Stinis
+ PDF Chat	Self-Adaptive Weights Based on Balanced Residual Decay Rate for Physics-Informed Neural Networks and Deep Operator Networks	2024	Wenqian Chen Amanda A. Howard Panos Stinis
+	Scientific machine learning for closure models in multiscale problems: a review	2024	Benjamin Sanderse Panos Stinis Romit Maulik Shady E. Ahmed
+	Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations	2023	Wenqian Chen Panos Stinis
+	Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model	2023	Wenqian Chen Yucheng Fu Panos Stinis
+	Multifidelity deep operator networks for data-driven and physics-informed problems	2023	Amanda A. Howard Mauro Perego George Em Karniadakis Panos Stinis
+ PDF Chat	A hybrid deep neural operator/finite element method for ice-sheet modeling	2023	Qizhi He Mauro Perego Amanda A. Howard George Em Karniadakis Panos Stinis
+ PDF Chat	A multifidelity deep operator network approach to closure for multiscale systems	2023	Shady E. Ahmed Panos Stinis
+ PDF Chat	Machine-learning-based spectral methods for partial differential equations	2023	Brek Meuris Saad Qadeer Panos Stinis
+	A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling	2023	Qizhi He Mauro Perego Amanda A. Howard George Em Karniadakis Panos Stinis
+	SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics	2023	Panos Stinis Constantinos Daskalakis Paul J. Atzberger
+	ViTO: Vision Transformer-Operator	2023	Oded Ovadia Adar Kahana Panos Stinis Eli Turkel George Em Karniadakis
+	A Multifidelity deep operator network approach to closure for multiscale systems	2023	Shady E. Ahmed Panos Stinis
+	Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations	2023	Wenqian Chen Panos Stinis
+	A multifidelity approach to continual learning for physical systems	2023	Amanda A. Howard Yucheng Fu Panos Stinis
+ PDF Chat	Feature-Adjacent Multi-Fidelity Physics-Informed Machine Learning for Partial Differential Equations	2023	Wenqian Chen Panos Stinis
+ PDF Chat	Sdyn-Gans: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics	2023	Panos Stinis Konstantinos S. Daskalakis Paul J. Atzberger
+	Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model	2023	Wenqian Chen Yucheng Fu Panos Stinis
+	Physics-informed machine learning of the correlation functions in bulk fluids	2023	Wenqian Chen Peiyuan Gao Panos Stinis
+	Exploring Learned Representations of Neural Networks with Principal Component Analysis	2023	Amit Harlev Andrew G. Engel Panos Stinis Tony Chiang
+	Efficient kernel surrogates for neural network-based regression	2023	Saad Qadeer Andrew G. Engel Adam Tsou Max Vargas Panos Stinis Tony Chiang
+	Stacked networks improve physics-informed training: applications to neural networks and deep operator networks	2023	Amanda A. Howard Sarah H. Murphy Shady E. Ahmed Panos Stinis
+	Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding	2023	Youngeun Kim Adar Kahana Ruokai Yin Yuhang Li Panos Stinis George Em Karniadakis Priyadarshini Panda
+	Physics-Guided Continual Learning for Accelerating Aqueous Organic Redox Flow Battery Material Discovery	2023	Yucheng Fu Amanda A. Howard Chao Zeng Panos Stinis
+ PDF Chat	Vibrational levels of a generalized Morse potential	2022	Saad Qadeer Garrett D. Santis Panos Stinis Sotiris S. Xantheas
+	Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery	2022	Qizhi He Yucheng Fu Panos Stinis Alexandre M. Tartakovsky
+ PDF Chat	Vibrational Levels of a Generalized Morse Potential	2022	Saad Qadeer Garrett D. Santis Panos Stinis Sotiris S. Xantheas
+ PDF Chat	Physics-constrained deep neural network method for estimating parameters in a redox flow battery	2022	Qizhi He Panos Stinis Alexandre M. Tartakovsky
+	Enhanced Physics-Constrained Deep Neural Networks for Modeling Vanadium Redox Flow Battery	2022	Qizhi He Yucheng Fu Panos Stinis Alexandre M. Tartakovsky
+	Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems	2022	Amanda A. Howard Mauro Perego George Em Karniadakis Panos Stinis
+	Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery	2022	Qizhi He Yucheng Fu Panos Stinis Alexandre M. Tartakovsky
+	SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations	2022	Qian Zhang Adar Kahana George Em Karniadakis Panos Stinis
+	Machine learning structure preserving brackets for forecasting irreversible processes	2021	Kookjin Lee Nathaniel Trask Panos Stinis
+	Machine-learning custom-made basis functions for partial differential equations.	2021	Brek Meuris Saad Qadeer Panos Stinis
+ PDF Chat	Optimal renormalization of multiscale systems	2021	Jacob Price Brek Meuris Madelyn Shapiro Panos Stinis
+	Machine learning structure preserving brackets for forecasting irreversible processes	2021	Kookjin Lee Nathaniel Trask Panos Stinis
+	Time-dependent stochastic basis adaptation for uncertainty quantification.	2021	Ramakrishna Tipireddy Panos Stinis Alexandre M. Tartakovsky
+	Optimal renormalization of multi-scale systems	2021	Jacob Price Brek Meuris Madelyn Shapiro Panos Stinis
+ PDF Chat	A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure	2021	J. P. Roth David A. Barajas‐Solano Panos Stinis Jonathan Weare Mihai Anitescu
+	Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling	2021	Kookjin Lee Nathaniel Trask Panos Stinis
+	Machine-learning custom-made basis functions for partial differential equations	2021	Brek Meuris Saad Qadeer Panos Stinis
+	Machine learning structure preserving brackets for forecasting irreversible processes	2021	Kookjin Lee Nathaniel Trask Panos Stinis
+	Time-dependent stochastic basis adaptation for uncertainty quantification	2021	Ramakrishna Tipireddy Panos Stinis Alexandre M. Tartakovsky
+	Physics-constrained deep neural network method for estimating parameters in a redox flow battery	2021	Qizhi He Panos Stinis Alexandre M. Tartakovsky
+ PDF Chat	Improving Solution Accuracy and Convergence for Stochastic Physics Parameterizations with Colored Noise	2020	Panos Stinis Huan Lei Jing Li Hui Wan
+	A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure	2019	J. P. Roth David A. Barajas‐Solano Panos Stinis Jonathan Weare Mihai Anitescu
+ PDF Chat	Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks	2019	Panos Stinis Tobias Hagge Alexandre M. Tartakovsky Enoch Yeung
+	A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations	2019	Ramakrishna Tipireddy Paris Perdikaris Panos Stinis Alexandre M. Tartakovsky
+	Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning	2019	Panos Stinis
+ PDF Chat	Renormalized Reduced Order Models with Memory for Long Time Prediction	2019	Jacob Price Panos Stinis
+ PDF Chat	Mori-Zwanzig reduced models for uncertainty quantification	2019	Jing Li Panos Stinis
+	Model reduction for a power grid model	2019	Jing Li Panos Stinis
+	A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure	2019	Jacob S. Roth David A. Barajas‐Solano Panos Stinis Jonathan Weare Mihai Anitescu
+ PDF Chat	Doing the Impossible: Why Neural Networks Can Be Trained at All	2018	Nathan O. Hodas Panos Stinis
+	Renormalization and blow-up for the 3D Euler equations	2018	Jacob Price Panos Stinis
+	Doing the impossible: Why neural networks can be trained at all	2018	Nathan O. Hodas Panos Stinis
+	Data-driven approach of quantifying uncertainty in complex systems with arbitrary randomness	2018	Huan Lei Jing Li Peiyuan Gao Panos Stinis Nathan Baker
+ PDF Chat	Dynamic Looping of a Free-Draining Polymer	2018	Felix X.-F. Ye Panos Stinis Hong Qian
+	Mori-Zwanzig reduced models for uncertainty quantification	2018	Jing Li Panos Stinis
+	Renormalization and blow-up for the 3D Euler equations	2018	Jacob Price Panos Stinis
+	Doing the impossible: Why neural networks can be trained at all	2018	Nathan O. Hodas Panos Stinis
+ PDF Chat	Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients	2017	Ramakrishna Tipireddy Panos Stinis Alexandre M. Tartakovsky
+	Renormalized Reduced Order Models with Memory for Long Time Prediction	2017	Jacob Price Panos Stinis
+	Stochastic basis adaptation and spatial domain decomposition for PDEs with random coefficients	2017	Ramakrishna Tipireddy Panos Stinis Alexandre M. Tartakovsky
+	Solving differential equations with unknown constitutive relations as recurrent neural networks	2017	Tobias Hagge Panos Stinis Enoch Yeung Alexandre M. Tartakovsky
+	Renormalized Reduced Order Models with Memory for Long Time Prediction	2017	Jacob Price Panos Stinis
+ PDF Chat	A unified framework for mesh refinement in random and physical space	2016	Jing Li Panos Stinis
+	Efficient failure probability calculation through mesh refinement	2015	Jing Li Panos Stinis
+ PDF Chat	Mesh refinement for uncertainty quantification through model reduction	2014	Jing Li Panos Stinis
+ PDF Chat	Renormalized reduced models for singular PDEs	2013	Panos Stinis
+ PDF Chat	Numerical Computation of Solutions of the Critical Nonlinear Schrödinger Equation after the Singularity	2012	Panos Stinis
+ PDF Chat	Stochastic global optimization as a filtering problem	2011	Panos Stinis
+	Path sampling for particle filters with application to multi-target tracking	2010	Vasileios Maroulas Panos Stinis
+	A Girsanov Monte Carlo approach to particle filtering for multi-target tracking	2010	Vasileios Maroulas Panos Stinis
+ PDF Chat	Variance Reduction for Particle Filters of Systems With Time Scale Separation	2008	Dror Givon Panos Stinis Jonathan Weare

Common Coauthors

Coauthor	Papers Together
Amanda A. Howard	17
Alexandre M. Tartakovsky	12
George Em Karniadakis	11
Yucheng Fu	9
Qizhi He	7
Shady E. Ahmed	6
Adar Kahana	6
Brek Meuris	6
Saad Qadeer	6
Ramakrishna Tipireddy	5
Jacob Price	5
Wenqian Chen	5
Peiyuan Gao	4
Kookjin Lee	4
Jonathan Weare	4
Nathaniel Trask	4
Wenqian Chen	4
Mauro Perego	4
Jing Li	4
Nathan O. Hodas	3
Mihai Anitescu	3
Bruno Jacob	3
Sarah H. Murphy	3
David A. Barajas‐Solano	3
Tony Chiang	2
J. P. Roth	2
Eli Turkel	2
Enoch Yeung	2
Ruokai Yin	2
Romit Maulik	2
Vasileios Maroulas	2
Madelyn Shapiro	2
Priyadarshini Panda	2
Qian Zhang	2
Yuhang Li	2
Tobias Hagge	2
Benjamin Sanderse	2
Chao Zeng	2
Andrew G. Engel	2
Paul J. Atzberger	2
Sotiris S. Xantheas	2
Jacob Price	2
Alexander Heinlein	2
Garrett D. Santis	2
Youngeun Kim	2
Damien Beecroft	1
Constantinos Daskalakis	1
Huan Lei	1
Felix X.-F. Ye	1
Yunxiang Chen	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Problem reduction, renormalization, and memory	2006	Alexandre J. Chorin Panagiotis Stinis	12
+ PDF Chat	Optimal prediction and the rate of decay for solutions of the Euler equations in two and three dimensions	2007	Ole H. Hald Panagiotis Stinis	9
+ PDF Chat	Extracting macroscopic dynamics: model problems and algorithms	2004	Dror Givon Raz Kupferman Andrew M. Stuart	8
+ PDF Chat	A phase transition approach to detecting singularities of partial differential equations	2009	Panagiotis Stinis	8
+ PDF Chat	Renormalized reduced models for singular PDEs	2013	Panos Stinis	8
+ PDF Chat	Long-time integration of parametric evolution equations with physics-informed DeepONets	2022	Sifan Wang Paris Perdikaris	6
+	Non-Markovian closure models for large eddy simulations using the Mori-Zwanzig formalism	2017	Eric Parish Karthik Duraisamy	6
+	When and why PINNs fail to train: A neural tangent kernel perspective	2021	Sifan Wang Xinling Yu Paris Perdikaris	5
+	DGM: A deep learning algorithm for solving partial differential equations	2018	Justin Sirignano Konstantinos Spiliopoulos	5
+ PDF Chat	Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport	2022	Lu Lu Raphaël Pestourie Steven G. Johnson Giuseppe Romano	5
+ PDF Chat	Higher Order Mori–Zwanzig Models for the Euler Equations	2007	Panagiotis Stinis	5
+	A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems	2019	Xuhui Meng George Em Karniadakis	5
+ PDF Chat	Data-driven parameterization of the generalized Langevin equation	2016	Huan Lei Nathan Baker Xiantao Li	5
+	Physics-informed neural networks (PINNs) for fluid mechanics: a review	2021	Shengze Cai Zhiping Mao Zhicheng Wang Minglang Yin George Em Karniadakis	5
+ PDF Chat	Learning the solution operator of parametric partial differential equations with physics-informed DeepONets	2021	Sifan Wang Hanwen Wang Paris Perdikaris	4
+	Fourier Neural Operator for Parametric Partial Differential Equations	2020	Zongyi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu Kaushik Bhattacharya Andrew M. Stuart Anima Anandkumar	4
+ PDF Chat	A multifidelity deep operator network approach to closure for multiscale systems	2023	Shady E. Ahmed Panos Stinis	4
+	Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets	2023	Subhayan De Matthew Reynolds Malik Hassanaly Ryan King Alireza Doostan	4
+ PDF Chat	Data-assisted reduced-order modeling of extreme events in complex dynamical systems	2018	Zhong Wan Pantelis R. Vlachas Petros Koumoutsakos Themistoklis P. Sapsis	4
+	Continual lifelong learning with neural networks: A review	2019	German I. Parisi Ronald Kemker Jose L. Part Christopher Kanan Stefan Wermter	4
+	Novel approach to nonlinear/non-Gaussian Bayesian state estimation	1993	Neil Gordon David Salmond A. F. M. Smith	4
+ PDF Chat	Multifidelity modeling for Physics-Informed Neural Networks (PINNs)	2021	Michael Penwarden Shandian Zhe Akil Narayan Robert M. Kirby	3
+	Improved Architectures and Training Algorithms for Deep Operator Networks	2022	Sifan Wang Hanwen Wang Paris Perdikaris	3
+ PDF Chat	A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations	2022	Revanth Mattey Susanta Ghosh	3
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	3
+ PDF Chat	DAE-PINN: a physics-informed neural network model for simulating differential algebraic equations with application to power networks	2022	Christian Moya Guang Lin	3
+	The FEniCS Project Version 1.5	2015	Martin Sandve Alnæs Jan Blechta Johan Hake August Johansson Benjamin Kehlet Anders Logg Chris Richardson Johannes Ring Marie E. Rognes Garth N. Wells	3
+ PDF Chat	Physics-constrained deep neural network method for estimating parameters in a redox flow battery	2022	Qizhi He Panos Stinis Alexandre M. Tartakovsky	3
+	Galerkin Neural Networks: A Framework for Approximating Variational Equations with Error Control	2021	Mark Ainsworth Justin Dong	3
+ PDF Chat	TRANSFER LEARNING ON MULTIFIDELITY DATA	2021	Dong Ho Song Daniel M. Tartakovsky	3
+ PDF Chat	Optimal renormalization of multiscale systems	2021	Jacob Price Brek Meuris Madelyn Shapiro Panos Stinis	3
+ PDF Chat	A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials	2022	Somdatta Goswami Minglang Yin Yue Yu George Em Karniadakis	3
+ PDF Chat	Why Does Deep and Cheap Learning Work So Well?	2017	Henry W. Lin Max Tegmark David Rolnick	3
+	Physics-informed neural networks for inverse problems in nano-optics and metamaterials	2020	Yuyao Chen Lu Lu George Em Karniadakis Luca Dal Negro	3
+ PDF Chat	Physics-Informed Neural Networks for Power Systems	2020	George S. Misyris Andreas Venzke Spyros Chatzivasileiadis	3
+	ON TRANSFER LEARNING OF NEURAL NETWORKS USING BI-FIDELITY DATA FOR UNCERTAINTY PROPAGATION	2020	Subhayan De Jolene Britton Matthew Reynolds Ryan Skinner Kenneth E. Jansen Alireza Doostan	3
+ PDF Chat	Transient Dynamics Increasing Network Vulnerability to Cascading Failures	2008	Ingve Simonsen Ľuboš Buzna Karsten Peters Stefan Bornholdt Dirk Helbing	3
+	Sequential Monte Carlo Methods for Dynamic Systems	1998	Jun S. Liu Rong Chen	3
+ PDF Chat	Error estimates for DeepONets: a deep learning framework in infinite dimensions	2022	Samuel Lanthaler Siddhartha Mishra George Em Karniadakis	3
+	Lagrangian and Geometric Analysis of Finite-time Euler Singularities	2013	Tobias Grafke Rainer Grauer	3
+ PDF Chat	Robustness of power-law behavior in cascading line failure models	2017	Fiona Sloothaak Sem Borst Bert Zwart	3
+ PDF Chat	PPINN: Parareal physics-informed neural network for time-dependent PDEs	2020	Xuhui Meng Zhen Li Dongkun Zhang George Em Karniadakis	3
+ PDF Chat	Development of high vorticity structures in incompressible 3D Euler equations	2015	D. S. Agafontsev E. A. Kuznetsov Alexei A. Mailybaev	3
+ PDF Chat	On the limited memory BFGS method for large scale optimization	1989	Cheng‐Di Dong Jorge Nocedal	3
+ PDF Chat	Nonparametric forecasting of low-dimensional dynamical systems	2015	Tyrus Berry Dimitrios Giannakis John Harlim	3
+ PDF Chat	Potentially singular solutions of the 3D axisymmetric Euler equations	2014	Guo Qing Luo Thomas Y. Hou	3
+	Neural ordinary differential equations	2018	Ricky T. Q. Chen Yulia Rubanova Jesse Bettencourt David Duvenaud	3
+	NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations	2020	Xiaowei Jin Shengze Cai Hui Li George Em Karniadakis	3
+	High-Order Collocation Methods for Differential Equations with Random Inputs	2005	Dongbin Xiu Jan S. Hesthaven	3
+	Neural Ordinary Differential Equations	2018	Ricky T. Q. Chen Yulia Rubanova Jesse Bettencourt David Duvenaud	3