Panos Stinis

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 43

Top Topics

Machine Learning

Mesh Refinement

Differential Equations

Top Venues

J. Comput. Phys.

Multiscale Model. Simul.

Publications

Wenqian Chen, Panos Stinis
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations. J. Comput. Phys. 498 (2024)
Benjamin Sanderse, Panos Stinis, Romit Maulik, Shady E. Ahmed
Scientific machine learning for closure models in multiscale problems: a review. CoRR (2024)
Wenqian Chen, Amanda A. Howard, Panos Stinis
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks. CoRR (2024)
Alexander Heinlein, Amanda A. Howard, Damien Beecroft, Panos Stinis
Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems. CoRR (2024)
Amanda A. Howard, Bruno Jacob, Sarah H. Murphy, Alexander Heinlein, Panos Stinis
Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems. CoRR (2024)
Wenqian Chen, Panos Stinis
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations. CoRR (2023)
Amit Harlev, Andrew Engel, Panos Stinis, Tony Chiang
Exploring Learned Representations of Neural Networks with Principal Component Analysis. CoRR (2023)
Amanda A. Howard, Yucheng Fu, Panos Stinis
A multifidelity approach to continual learning for physical systems. CoRR (2023)
Amanda A. Howard, Sarah H. Murphy, Shady E. Ahmed, Panos Stinis
Stacked networks improve physics-informed training: applications to neural networks and deep operator networks. CoRR (2023)
QiZhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis
A hybrid deep neural operator/finite element method for ice-sheet modeling. J. Comput. Phys. 492 (2023)
Wenqian Chen, Peiyuan Gao, Panos Stinis
Physics-informed machine learning of the correlation functions in bulk fluids. CoRR (2023)
Saad Qadeer, Andrew Engel, Adam Tsou, Max Vargas, Panos Stinis, Tony Chiang
Efficient kernel surrogates for neural network-based regression. CoRR (2023)
Amanda A. Howard, Mauro Perego, George Em Karniadakis, Panos Stinis
Multifidelity deep operator networks for data-driven and physics-informed problems. J. Comput. Phys. 493 (2023)
Oded Ovadia, Adar Kahana, Panos Stinis, Eli Turkel, George Em Karniadakis
ViTO: Vision Transformer-Operator. CoRR (2023)
Panos Stinis, Constantinos Daskalakis, Paul J. Atzberger
SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics. CoRR (2023)
Wenqian Chen, Yucheng Fu, Panos Stinis
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model. CoRR (2023)
QiZhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling. CoRR (2023)
Shady E. Ahmed, Panos Stinis
A Multifidelity deep operator network approach to closure for multiscale systems. CoRR (2023)
Amanda A. Howard, Mauro Perego, George E. Karniadakis, Panos Stinis
Multifidelity Deep Operator Networks. CoRR (2022)
QiZhi He, Yucheng Fu, Panos Stinis, Alexandre M. Tartakovsky
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery. CoRR (2022)
Qian Zhang, Adar Kahana, George Em Karniadakis, Panos Stinis
SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations. CoRR (2022)
Kookjin Lee, Nathaniel Trask, Panos Stinis
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling. MSML (2022)
Jacob Roth, David A. Barajas-Solano, Panos Stinis, Jonathan Weare, Mihai Anitescu
A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure. Multiscale Model. Simul. 19 (1) (2021)
QiZhi He, Panos Stinis, Alexandre M. Tartakovsky
Physics-constrained deep neural network method for estimating parameters in a redox flow battery. CoRR (2021)
Brek Meuris, Saad Qadeer, Panos Stinis
Machine-learning custom-made basis functions for partial differential equations. CoRR (2021)
Jacob Price, Brek Meuris, Madelyn Shapiro, Panos Stinis
Optimal renormalization of multi-scale systems. CoRR (2021)
Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
Time-dependent stochastic basis adaptation for uncertainty quantification. CoRR (2021)
Kookjin Lee, Nathaniel A. Trask, Panos Stinis
Machine learning structure preserving brackets for forecasting irreversible processes. CoRR (2021)
Kookjin Lee, Nathaniel Trask, Panos Stinis
Machine learning structure preserving brackets for forecasting irreversible processes. NeurIPS (2021)
Kookjin Lee, Nathaniel Trask, Panos Stinis
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling. CoRR (2021)
Panos Stinis
Enforcing Constraints for Time Series Prediction in Supervised, Unsupervised and Reinforcement Learning. AAAI Spring Symposium: MLPS (2020)
Jacob Price, Panos Stinis
Renormalized Reduced Order Models with Memory for Long Time Prediction. Multiscale Model. Simul. 17 (1) (2019)
Jing Li, Panos Stinis
Model reduction for a power grid model. CoRR (2019)
Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. J. Comput. Phys. 397 (2019)
Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre M. Tartakovsky
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations. CoRR (2019)
Panos Stinis
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning. CoRR (2019)
Felix X.-F. Ye, Panos Stinis, Hong Qian
Dynamic Looping of a Free-Draining Polymer. SIAM J. Appl. Math. 78 (1) (2018)
Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. CoRR (2018)
Nathan O. Hodas, Panos Stinis
Doing the impossible: Why neural networks can be trained at all. CoRR (2018)
Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
Stochastic Basis Adaptation and Spatial Domain Decomposition for Partial Differential Equations with Random Coefficients. SIAM/ASA J. Uncertain. Quantification 6 (1) (2018)
Tobias Hagge, Panos Stinis, Enoch Yeung, Alexandre M. Tartakovsky
Solving differential equations with unknown constitutive relations as recurrent neural networks. CoRR (2017)
Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky
Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients. J. Comput. Phys. 351 (2017)
Jing Li, Panos Stinis
A unified framework for mesh refinement in random and physical space. J. Comput. Phys. 323 (2016)
Jing Li, Panos Stinis
Mesh refinement for uncertainty quantification through model reduction. J. Comput. Phys. 280 (2015)
Panos Stinis
Stochastic global optimization as a filtering problem. J. Comput. Phys. 231 (4) (2012)
Vasileios Maroulas, Panos Stinis
Improved particle filters for multi-target tracking. J. Comput. Phys. 231 (2) (2012)
Panos Stinis
Numerical Computation of Solutions of the Critical Nonlinear Schrödinger Equation after the Singularity. Multiscale Model. Simul. 10 (1) (2012)