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Fundamental and Computational Sciences Directorate

Staff information

Panos Stinis

Advanced Computing, Mathematics and Data
Pacific Northwest National Laboratory
PO Box 999
MSIN: K7-90
Richland, WA 99352

PNNL Publications


  • Meuris B., S. Qadeer, and P. Stinis. 2023. "Machine-learning-based spectral methods for partial differential equations." Scientific Reports 13, no. 1:Art. No. 1739. PNNL-SA-168281. doi:10.1038/s41598-022-26602-3


  • He Q., P. Stinis, and A.M. Tartakovsky. 2022. "Physics-constrained deep neural network method for estimating parameters in the redox flow battery." Journal of Power Sources 528. PNNL-SA-163269. doi:10.1016/j.jpowsour.2022.231147
  • Li J., and P. Stinis. 2022. "Model reduction for a power grid model." Journal of Computational Dynamics 9, no. 1:1-26. PNNL-SA-150116. doi:10.3934/jcd.2021019
  • Qadeer S., G.D. Santis, P. Stinis, and S.S. Xantheas. 2022. "Vibrational Levels of a Generalized Morse Potential." The Journal of Chemical Physics 157, no. 14:Art. No. 144104. PNNL-SA-174299. doi:10.1063/5.0103433
  • Tipireddy R., P. Perdikaris, P. Stinis, and A.M. Tartakovsky. 2022. "Multistep and continuous physics-informed neural network methods for learning governing equations and constitutive relations." Journal of Machine Learning for Modeling and Computing 3, no. 2:23-46. PNNL-SA-177559. doi:10.1615/JMachLearnModelComput.2022041787


  • Price J., B. Meuris, M.R. Shapiro, and P. Stinis. 2021. "Optimal renormalization of multiscale systems." Proceedings of the National Academy of Sciences (PNAS) 118, no. 37:Article No. e2102266118. PNNL-SA-159021. doi:10.1073/pnas.2102266118
  • Roth J., D.A. Barajas-Solano, P. Stinis, J.Q. Weare, and M. Anitescu. 2021. "A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure." Multiscale Modeling & Simulation 19, no. 1:208-241. PNNL-SA-150017. doi:10.1137/19M1306865


  • Stinis P. 2020. "Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning." In Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences (AAAI-MLPS 2020), March 23-25, 2020, Stanford, CA, edited by J. Lee, et al, 2587, Paper No. 5. Aachen:CEUR Workshop Proceedings/RWTH Aachen University. PNNL-SA-143654.
  • Stinis P., H. Lei, J. Li, and H. Wan. 2020. "Improving solution accuracy and convergence for stochastic physics parameterizations with colored noise." Monthly Weather Review 148, no. 6:2251-2263. PNNL-SA-142475. doi:10.1175/MWR-D-19-0178.1
  • Vogl C.J., H. Wan, S. Zhang, C. Woodward, and P. Stinis. 2020. "Improving time step convergence in an atmosphere model with simplified physics: using mathematical rigor to avoid nonphysical behavior in a parameterization." Journal of Advances in Modeling Earth Systems 12, no. 10:Article No. e2019MS001974. PNNL-SA-149966. doi:10.1029/2019MS001974
  • Wan H., C. Woodward, S. Zhang, C.J. Vogl, P. Stinis, D. Gardner, and P.J. Rasch, et al. 2020. "Improving time step convergence in an atmosphere model with simplified physics: the impacts of closure assumption and process coupling." Journal of Advances in Modeling Earth Systems 12, no. 10:Article No. e2019MS001982. PNNL-SA-149970. doi:10.1029/2019MS001982


  • Lei H., J. Li, P. Gao, P. Stinis, and N.A. Baker. 2019. "A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness." Computer Methods in Applied Mechanics and Engineering 350. PNNL-SA-134010. doi:10.1016/j.cma.2019.03.014
  • Li J., and P. Stinis. 2019. "Mori-Zwanzig reduced models for uncertainty quantification." Journal of Computational Dynamics 6, no. 1:39-68. PNNL-SA-132853. doi:10.3934/jcd.2019002
  • Price J., and P. Stinis. 2019. "Renormalized Reduced Order Models with Memory for Long Time Prediction." Multiscale Modeling & Simulation 17, no. 1:68-91. PNNL-SA-127388. doi:10.1137/17M1151389
  • Stinis P., T.J. Hagge, A.M. Tartakovsky, and E.H. Yeung. 2019. "Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks." Journal of Computational Physics 397. PNNL-SA-133233. doi:10.1016/


  • Hodas N.O., and P. Stinis. 2018. "Doing the impossible: Why neural networks can be trained at all." Frontiers in Psychology 9, no. JUN:Article No. 1185. PNNL-SA-127608. doi:10.3389/fpsyg.2018.01185
  • Tipireddy R., P. Stinis, and A.M. Tartakovsky. 2018. "Stochastic basis adaptation and spatial domain decomposition for partial differential equations with random coefficients." SIAM/ASA Journal on Uncertainty Quantification 6, no. 1:273-301. PNNL-SA-121137. doi:10.1137/16M1097134
  • Ye X., P. Stinis, and H. Qian. 2018. "Dynamic Looping of a Free-draining Polymer." SIAM Journal on Applied Mathematics 78, no. 1:104-123. PNNL-SA-125393. doi:10.1137/17M1127260


  • Hagge T.J., P. Stinis, E.H. Yeung, and A.M. Tartakovsky. 2017. "Solving differential equations with unknown constitutive relations as recurrent neural networks." In Deep Learning for the Physical Sciences (NIPS Workshop), December 8, 2017, Long Beach, California. La Jolla, California:Neural Information Processing Systems Foundation, Inc. PNNL-SA-130320.
  • Tipireddy R., P. Stinis, and A.M. Tartakovsky. 2017. "Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients." Journal of Computational Physics 351. PNNL-SA-115134. doi:10.1016/


  • Li J., and P. Stinis. 2016. "A unified framework for mesh refinement in random and physical space." Journal of Computational Physics 323. PNNL-SA-112716. doi:10.1016/

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