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2020 – today
- 2024
[j18]Wenqian Chen, Panos Stinis:
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations. J. Comput. Phys. 498: 112683 (2024)- [j17]Panos Stinis, C. Daskalakis, Paul J. Atzberger
:
SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics. J. Comput. Phys. 519: 113442 (2024) - [i34]Alexander Heinlein, Amanda A. Howard, Damien Beecroft, Panos Stinis:
Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems. CoRR abs/2401.07888 (2024) - [i33]Benjamin Sanderse, Panos Stinis, Romit Maulik, Shady E. Ahmed:
Scientific machine learning for closure models in multiscale problems: a review. CoRR abs/2403.02913 (2024) - [i32]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 abs/2406.19662 (2024) - [i31]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 abs/2407.01613 (2024) - [i30]Amanda A. Howard, Bruno Jacob, Panos Stinis:
Multifidelity Kolmogorov-Arnold Networks. CoRR abs/2410.14764 (2024) - [i29]Bruno Jacob, Amanda A. Howard, Panos Stinis:
SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks. CoRR abs/2411.06286 (2024) - [i28]Emily Williams, Amanda A. Howard, Brek Meuris, Panos Stinis:
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications. CoRR abs/2411.18459 (2024) - 2023
- [j16]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: 112428 (2023) - [j15]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: 112462 (2023) - [i27]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 abs/2301.11402 (2023) - [i26]Panos Stinis, Constantinos Daskalakis, Paul J. Atzberger:
SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics. CoRR abs/2302.03663 (2023) - [i25]Oded Ovadia, Adar Kahana, Panos Stinis, Eli Turkel, George Em Karniadakis:
ViTO: Vision Transformer-Operator. CoRR abs/2303.08891 (2023) - [i24]Shady E. Ahmed, Panos Stinis:
A Multifidelity deep operator network approach to closure for multiscale systems. CoRR abs/2303.08893 (2023) - [i23]Wenqian Chen, Panos Stinis:
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations. CoRR abs/2303.11577 (2023) - [i22]Amanda A. Howard, Yucheng Fu, Panos Stinis:
A multifidelity approach to continual learning for physical systems. CoRR abs/2304.03894 (2023) - [i21]Wenqian Chen, Yucheng Fu, Panos Stinis:
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model. CoRR abs/2306.01010 (2023) - [i20]Wenqian Chen, Peiyuan Gao, Panos Stinis:
Physics-informed machine learning of the correlation functions in bulk fluids. CoRR abs/2309.00767 (2023) - [i19]Amit Harlev, Andrew Engel, Panos Stinis, Tony Chiang:
Exploring Learned Representations of Neural Networks with Principal Component Analysis. CoRR abs/2309.15328 (2023) - [i18]Saad Qadeer, Andrew Engel, Adam Tsou, Max Vargas, Panos Stinis, Tony Chiang:
Efficient kernel surrogates for neural network-based regression. CoRR abs/2310.18612 (2023) - [i17]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 abs/2311.06483 (2023) - 2022
- [c3]Kookjin Lee, Nathaniel Trask, Panos Stinis:
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling. MSML 2022: 65-80 - [i16]QiZhi He, Yucheng Fu, Panos Stinis, Alexandre M. Tartakovsky:
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery. CoRR abs/2203.01985 (2022) - [i15]Amanda A. Howard, Mauro Perego, George E. Karniadakis, Panos Stinis:
Multifidelity Deep Operator Networks. CoRR abs/2204.09157 (2022) - [i14]Qian Zhang, Adar Kahana, George Em Karniadakis, Panos Stinis:
SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations. CoRR abs/2211.09928 (2022) - 2021
- [j14]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): 208-241 (2021) - [c2]Kookjin Lee, Nathaniel Trask, Panos Stinis:
Machine learning structure preserving brackets for forecasting irreversible processes. NeurIPS 2021: 5696-5707 - [i13]Jacob Price, Brek Meuris, Madelyn Shapiro, Panos Stinis:
Optimal renormalization of multi-scale systems. CoRR abs/2101.09789 (2021) - [i12]Ramakrishna Tipireddy, Panos Stinis, Alexandre M. Tartakovsky:
Time-dependent stochastic basis adaptation for uncertainty quantification. CoRR abs/2103.03316 (2021) - [i11]QiZhi He, Panos Stinis, Alexandre M. Tartakovsky:
Physics-constrained deep neural network method for estimating parameters in a redox flow battery. CoRR abs/2106.11451 (2021) - [i10]Kookjin Lee, Nathaniel A. Trask, Panos Stinis:
Machine learning structure preserving brackets for forecasting irreversible processes. CoRR abs/2106.12619 (2021) - [i9]Kookjin Lee, Nathaniel Trask, Panos Stinis:
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling. CoRR abs/2109.05364 (2021) - [i8]Brek Meuris, Saad Qadeer, Panos Stinis:
Machine-learning custom-made basis functions for partial differential equations. CoRR abs/2111.05307 (2021) - 2020
- [c1]Panos Stinis:
Enforcing Constraints for Time Series Prediction in Supervised, Unsupervised and Reinforcement Learning. AAAI Spring Symposium: MLPS 2020
2010 – 2019
- 2019
- [j13]Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung:
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. J. Comput. Phys. 397 (2019) - [j12]Jacob Price, Panos Stinis:
Renormalized Reduced Order Models with Memory for Long Time Prediction. Multiscale Model. Simul. 17(1): 68-91 (2019) - [i7]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 abs/1904.04058 (2019) - [i6]Panos Stinis:
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning. CoRR abs/1905.07501 (2019) - [i5]Jacob Roth, David A. Barajas-Solano, Panagiotis Stinis, Jonathan Weare, Mihai Anitescu:
A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure. CoRR abs/1912.08081 (2019) - [i4]Jing Li, Panos Stinis:
Model reduction for a power grid model. CoRR abs/1912.12163 (2019) - 2018
- [j11]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): 273-301 (2018) - [j10]Felix X.-F. Ye, Panos Stinis, Hong Qian:
Dynamic Looping of a Free-Draining Polymer. SIAM J. Appl. Math. 78(1): 104-123 (2018) - [i3]Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung:
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. CoRR abs/1803.08182 (2018) - [i2]Nathan O. Hodas, Panos Stinis:
Doing the impossible: Why neural networks can be trained at all. CoRR abs/1805.04928 (2018) - 2017
- [j9]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: 203-215 (2017) - [i1]Tobias Hagge, Panos Stinis, Enoch Yeung, Alexandre M. Tartakovsky:
Solving differential equations with unknown constitutive relations as recurrent neural networks. CoRR abs/1710.02242 (2017) - 2016
- [j8]Jing Li, Panos Stinis:
A unified framework for mesh refinement in random and physical space. J. Comput. Phys. 323: 243-264 (2016) - 2015
- [j7]Jing Li, Panos Stinis:
Mesh refinement for uncertainty quantification through model reduction. J. Comput. Phys. 280: 164-183 (2015) - 2012
- [j6]Vasileios Maroulas, Panos Stinis:
Improved particle filters for multi-target tracking. J. Comput. Phys. 231(2): 602-611 (2012) - [j5]Panos Stinis:
Stochastic global optimization as a filtering problem. J. Comput. Phys. 231(4): 2002-2014 (2012) - [j4]Panos Stinis:
Numerical Computation of Solutions of the Critical Nonlinear Schrödinger Equation after the Singularity. Multiscale Model. Simul. 10(1): 48-60 (2012)
2000 – 2009
- 2009
- [j3]Dror Givon, Panagiotis Stinis, Jonathan Weare:
Variance Reduction for Particle Filters of Systems With Time Scale Separation. IEEE Trans. Signal Process. 57(2): 424-435 (2009) - 2007
- [j2]Panagiotis Stinis:
Higher Order Mori-Zwanzig Models for the Euler Equations. Multiscale Model. Simul. 6(3): 741-760 (2007) - 2004
- [j1]Panagiotis Stinis:
Stochastic Optimal Prediction for the Kuramoto-Sivashinsky Equation. Multiscale Model. Simul. 2(4): 580-612 (2004)
Coauthor Index
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