Panos Stinis

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A unified framework for mesh refinement in random and physical space

Jing Li, Panos Stinis, P. Stinis, 2015, J. Comput. Phys..

CONDITIONAL PATH SAMPLING FOR STOCHASTIC DIFFERENTIAL EQUATIONS THROUGH DRIFT RELAXATION

Panos Stinis, P. Stinis, 2011 .

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

Ramakrishna Tipireddy, Alexandre M. Tartakovsky, Panos Stinis, 2018, SIAM/ASA J. Uncertain. Quantification.

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

Paris Perdikaris, Ramakrishna Tipireddy, Alexandre M. Tartakovsky, 2019, ArXiv.

Dynamic Looping of a Free-Draining Polymer

Hong Qian, Panos Stinis, Felix X.-F. Ye, 2017, SIAM J. Appl. Math..

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

Ramakrishna Tipireddy, Alexandre M. Tartakovsky, Panos Stinis, 2017, J. Comput. Phys..

Stochastic global optimization as a filtering problem

Panos Stinis, P. Stinis, 2009, J. Comput. Phys..

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

Alexandre M. Tartakovsky, Enoch Yeung, Panos Stinis, 2017, ArXiv.

Improving Solution Accuracy and Convergence for Stochastic Physics Parameterizations with Colored Noise

Jing Li, Hui Wan, Huan Lei, 2019, Monthly Weather Review.

Doing the Impossible: Why Neural Networks Can Be Trained at All

Panos Stinis, Nathan O. Hodas, Nathan Oken Hodas, 2018, Front. Psychol..

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

Panos Stinis, P. Stinis, 2019, AAAI Spring Symposium: MLPS.

Efficient failure probability calculation through mesh refinement

Jing Li, Panos Stinis, P. Stinis, 2015, 1509.06668.

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

Panos Stinis, P. Stinis, 2010, Multiscale Model. Simul..

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness.

Jing Li, Nathan A. Baker, Huan Lei, 2018, Computer methods in applied mechanics and engineering.

Model reduction for a power grid model pdf

Jing Li, Panos Stinis, P. Stinis, 2019, Journal of Computational Dynamics.

Mesh refinement for uncertainty quantification through model reduction

Jing Li, Panos Stinis, 2015, J. Comput. Phys..

Renormalized Reduced Order Models with Memory for Long Time Prediction

Panos Stinis, Jacob Price, P. Stinis, 2017, Multiscale Model. Simul..

Improved particle filters for multi-target tracking

Vasileios Maroulas, Panos Stinis, V. Maroulas, 2012, J. Comput. Phys..

Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks pdf

Alexandre M. Tartakovsky, Enoch Yeung, Panos Stinis, 2018, J. Comput. Phys..

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

Alexandre Tartakovsky, Panos Stinis, QiZhi He, 2021, ArXiv.

Machine learning structure preserving brackets for forecasting irreversible processes pdf

Panos Stinis, Kookjin Lee, Nathaniel A. Trask, 2021, NeurIPS.

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

Nathaniel Trask, Panos Stinis, Kookjin Lee, 2021, MSML.

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

Saad Qadeer, Panos Stinis, Brek Meuris, 2021, ArXiv.

Time-dependent stochastic basis adaptation for uncertainty quantification pdf

Ramakrishna Tipireddy, Alexandre M. Tartakovsky, Panos Stinis, 2021, ArXiv.

Mori-Zwanzig reduced models for uncertainty quantification

Jing Li, Panos Stinis, P. Stinis, 2018, 1803.02826.

Renormalized Mori–Zwanzig-reduced models for systems without scale separation

Panos Stinis, P. Stinis, 2015, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.