Stochastic Optimal Prediction for the Kuramoto-Sivashinsky Equation. - Panagiotis Stinis (Multiscale Model. Simul., 2004)

Higher Order Mori-Zwanzig Models for the Euler Equations. - Panagiotis Stinis (Multiscale Model. Simul., 2007)

Variance Reduction for Particle Filters of Systems With Time Scale Separation. - Dror Givon; Panagiotis Stinis; Jonathan Weare (IEEE Trans. Signal Process., 2009)

Improved particle filters for multi-target tracking. - Vasileios Maroulas; Panos Stinis (J. Comput. Phys., 2012)

Stochastic global optimization as a filtering problem. - Panos Stinis (J. Comput. Phys., 2012)

Numerical Computation of Solutions of the Critical Nonlinear Schrödinger Equation after the Singularity. - Panos Stinis (Multiscale Model. Simul., 2012)

Mesh refinement for uncertainty quantification through model reduction. - Jing Li; Panos Stinis (J. Comput. Phys., 2015)

A unified framework for mesh refinement in random and physical space. - Jing Li; Panos Stinis (J. Comput. Phys., 2016)

Solving differential equations with unknown constitutive relations as recurrent neural networks. - Tobias Hagge; Panos Stinis; Enoch Yeung; Alexandre M. Tartakovsky (CoRR, 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)

Doing the impossible: Why neural networks can be trained at all. - Nathan O. Hodas; Panos Stinis (CoRR, 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)

Dynamic Looping of a Free-Draining Polymer. - Felix X.-F. Ye; Panos Stinis; Hong Qian (SIAM J. Appl. Math., 2018)

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)

Model reduction for a power grid model. - Jing Li; Panos Stinis (CoRR, 2019)

Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. - Panos Stinis; Tobias Hagge; Alexandre M. Tartakovsky; Enoch Yeung (J. Comput. Phys., 2019)

Renormalized Reduced Order Models with Memory for Long Time Prediction. - Jacob Price; Panos Stinis (Multiscale Model. Simul., 2019)

Enforcing Constraints for Time Series Prediction in Supervised, Unsupervised and Reinforcement Learning. - Panos Stinis (AAAI Spring Symposium: MLPS, 2020)

Machine learning structure preserving brackets for forecasting irreversible processes. - Kookjin Lee; Nathaniel Trask; Panos Stinis (NeurIPS, 2021)

Optimal renormalization of multi-scale systems. - Jacob Price; Brek Meuris; Madelyn Shapiro; Panos Stinis (CoRR, 2021)

Time-dependent stochastic basis adaptation for uncertainty quantification. - Ramakrishna Tipireddy; Panos Stinis; Alexandre M. Tartakovsky (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)

Machine-learning custom-made basis functions for partial differential equations. - Brek Meuris; Saad Qadeer; Panos Stinis (CoRR, 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)

Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling. - Kookjin Lee; Nathaniel Trask; Panos Stinis (MSML, 2022)

Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery. - QiZhi He; Yucheng Fu; Panos Stinis; Alexandre M. Tartakovsky (CoRR, 2022)

Multifidelity Deep Operator Networks. - Amanda A. Howard; Mauro Perego; George E. Karniadakis; Panos Stinis (CoRR, 2022)

SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations. - Qian Zhang; Adar Kahana; George Em Karniadakis; Panos Stinis (CoRR, 2022)

SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics. - Panos Stinis; Constantinos Daskalakis; Paul J. Atzberger (CoRR, 2023)

ViTO: Vision Transformer-Operator. - Oded Ovadia; Adar Kahana; Panos Stinis; Eli Turkel; George Em Karniadakis (CoRR, 2023)

A Multifidelity deep operator network approach to closure for multiscale systems. - Shady E. Ahmed; Panos Stinis (CoRR, 2023)

A multifidelity approach to continual learning for physical systems. - Amanda A. Howard; Yucheng Fu; 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)

Physics-informed machine learning of the correlation functions in bulk fluids. - Wenqian Chen; Peiyuan Gao; Panos Stinis (CoRR, 2023)

Exploring Learned Representations of Neural Networks with Principal Component Analysis. - Amit Harlev; Andrew Engel; Panos Stinis; Tony Chiang (CoRR, 2023)

Efficient kernel surrogates for neural network-based regression. - Saad Qadeer; Andrew Engel; Adam Tsou; Max Vargas; Panos Stinis; Tony Chiang (CoRR, 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)

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., 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., 2023)

Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems. - Alexander Heinlein; Amanda A. Howard; Damien Beecroft; Panos Stinis (CoRR, 2024)

Scientific machine learning for closure models in multiscale problems: a review. - Benjamin Sanderse; Panos Stinis; Romit Maulik; Shady E. Ahmed (CoRR, 2024)

Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems. - Amanda A. Howard; Bruno Jacob; Sarah H. Murphy; Alexander Heinlein; Panos Stinis (CoRR, 2024)

Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks. - Wenqian Chen; Amanda A. Howard; Panos Stinis (CoRR, 2024)

Multifidelity Kolmogorov-Arnold Networks. - Amanda A. Howard; Bruno Jacob; Panos Stinis (CoRR, 2024)

SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks. - Bruno Jacob; Amanda A. Howard; Panos Stinis (CoRR, 2024)

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications. - Emily Williams; Amanda A. Howard; Brek Meuris; Panos Stinis (CoRR, 2024)

SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics. - Panos Stinis; C. Daskalakis; Paul J. Atzberger (J. Comput. Phys., 2024)

Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations. - Wenqian Chen; Panos Stinis (J. Comput. Phys., 2024)

E-PINNs: Epistemic Physics-Informed Neural Networks. - Ashish S. Nair; Bruno Jacob; Amanda A. Howard; Jan Drgona; Panos Stinis (CoRR, 2025)

Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning. - Amirhossein Mollaali; Christian B. Moya; Amanda A. Howard; Alexander Heinlein; Panos Stinis; Guang Lin 001 (CoRR, 2025)

Stabilizing PDE-ML Coupled System. - Saad Qadeer; Panos Stinis; Hui Wan (CoRR, 2025)

Simulating Three-dimensional Turbulence with Physics-informed Neural Networks. - Sifan Wang; Shyam Sankaran; Panos Stinis; Paris Perdikaris (CoRR, 2025)