Panos Stinis
Panos Stinis
Biography
Panos Stinis specializes in scientific computing with application interests in model reduction of complex systems, multiscale modeling, uncertainty quantification, and machine learning.
Stinis studied aeronautical engineering at the Technical University of Athens, Greece. He earned his PhD in applied mathematics in 2003, from Columbia University in New York, in the area of model reduction. He began his career at Lawrence Berkeley National Laboratory and the Stanford Center for Turbulence Research, where he worked on applying model reduction methods to hyperbolic systems and in developing techniques for locating and tracking singularities of partial differential equations. In 2008, he became a faculty member at the Mathematics Department at the University of Minnesota, where he worked on renormalization, mesh refinement, and particle filtering and optimization.
He moved to PNNL in 2014, where he is currently leading the Computational Mathematics group. His recent research interests include multiscale modeling, uncertainty quantification, and machine learning. He serves as the codirector of the PhysicsInformed Learning Machines (PhILMs) multiinstitution collaboration and leads the Digital Twin Component Development thrust of the Energy Storing Materials Initiative.
Disciplines and Skills

Model reduction

Multiscale modeling

Uncertainty quantification

Machine learning
Education
PhD in Applied Mathematics, Columbia University, 2003
Diploma in Aeronautical Engineering, National Technical University of Athens, 1996
Publications
2021
He Q, P Stinis and A Tartakovsky. 2021. “Physicsconstrained deep neural network method for estimating parameters in a redox flow battery.” arXiv:2106.11451.
Lee K, N Trask and P Stinis. 2021. “Machine learning structure preserving brackets for forecasting irreversible Processes.” arXiv:2106.12619.
Price J, B Meuris, M Shapiro and P Stinis. 2021. “Optimal renormalization of multiscale systems.” Proceedings of the National Academy of Sciences, 118(37). DOI: 10.1073/pnas.2102266118.
Roth J, D BarajasSolano, P Stinis, J Weare and M Anitescu. 2021. “A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure.” Multiscale Modeling and Simulation, 19(1), pp. 208241. DOI:10.1137/19M1306865.
Tipireddy R, P Stinis and A Tartakovsky. 2021. “Timedependent stochastic basis adaptation for uncertainty quantification.” arXiv:2103.03316.
2020
Stinis P. 2020. “Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning.” Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, CEUR Workshop Proceedings, Vol. 2587.
Stinis P, H Lei, J Li and H Wan. 2020. “Improving solution accuracy and convergence for stochastic physics parameterizations with colored noise.” MonthlyWeather Review, 148(6). DOI:10.1175/MWRD190178.1.
Vogl CJ, H Wan, S Zhang, C Woodward, and P Stinis. 2020. “Improving timestep 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(10). DOI:10.1029/2019MS001974.
Wan H, C Woodward, S Zhang, CJ Vogl, P Stinis, D Gardner, and PJ Rasch. 2020. “Improving timestep convergence in an atmosphere model with simplified physics: the impacts of closure assumption and process coupling.” Journal of Advances in Modeling Earth Systems, 12(1). DOI:10.1029/2019MS001982.
2019
Lei H, J Li, P Gao, P Stinis and N Baker. 2019. “A datadriven framework for sparsityenhanced surrogates with arbitrary mutually dependent randomness.” Computer Methods in Applied Mechanics and Engineering, 350, pp. 199227. DOI:10.1016/j.cma.2019.03.014.
Li J and P Stinis. 2019. “Model reduction for a power grid model.” arXiv:1912.12163.
Li J and P Stinis. 2019. “MoriZwanzig reduced models for uncertainty quantification.” Journal of Computational Dynamics 6(1), pp. 3968. DOI:10.3934/jcd.2019002.
Price J and P Stinis. 2019. “Renormalized reduced order models with memory for long time prediction.” Multiscale Modeling and Simulation, 17(1), pp. 6891. DOI:10.1137/17M1151389.
Stinis P, T Hagge, AM Tartakovsky and E Yeung. 2019. “Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks.” Journal of Computational Physics, 397. DOI:10.1016/j.jcp.2019.07.042.
2018
Hodas N and P Stinis. 2018. “Doing the impossible: Why neural networks can be trained at all.” Frontiers in Psychology. DOI: 10.3389/fpsyg.2018.01185.
Price J and P. Stinis. 2018. “Renormalization and blowup for the 3D Euler equations.” arXiv:1805.08766.
Tipireddy R, P Stinis and A Tartakovsky. 2018. “Stochastic basis adaptation and spatial domain decomposition for PDEs with random coefficients.” SIAM Journal of Uncertainty Quantification, Vol. 6, No. 1, pp. 273301. DOI: 101137/16M1097134.
Ye F, P Stinis and H Qian. 2018. “Dynamic Looping of a FreeDraining Polymer.” SIAM Journal of Applied Mathematics, Vol. 78, No. 1, pp. 104123. DOI: 10.1137/17M1127260.
2017
Hagge T, E Yeung, P Stinis and A Tartakovsky. 2017. “Solving differential equations with unknown constitutive relations as recurrent neural networks.” arXiv:1710.02242.
Tipireddy R, P Stinis and A Tartakovsky. 2017. “Basis adaptation and domain decomposition for steady partial differential equations with random coefficients.” Journal of Computational Physics, 351, pp. 203215. DOI: 10.1016/j.jcp.2017.08.067.
2016
Li J and P. Stinis. 2016. “A unified framework for mesh refinement in random and physical space.” Journal of Computational Physics, Vol. 323 pp. 243264. DOI: 10.1016/j.jcp.2016.07.027.
2015
Li J and P Stinis. 2015. “Mesh refinement for uncertainty quantification through model reduction.” Journal of Computational Physics, 280, pp. 164183. DOI: 10.1016/j.jcp.2014.09.021.
Li J and P Stinis. 2015. “Efficient failure probability calculation through mesh refinement.” arXiv:1509.06668.
Stinis P. 2015. “Renormalized MoriZwanzig reduced models for systems without scale separation.” Proceedings of the Royal Society A Vol. 471 No. 2176. DOI: 10.1098/rspa.2014.0446.
2014
Stinis P. 2014. “Model reduction and mesh refinement.” arXiv:1402.6402v1.
2013
Stinis P. 2013. “Renormalized reduced models for singular PDEs.” Communications in Applied Mathematics and Computational Science, Vol. 8, No. 1, pp. 3966. DOI: 10.2140/camcos.2013.8.39.
2012
Maroulas V and P Stinis. 2012. “Improved particle filters for multitarget tracking.” Journal of Computational Physics, 231, pp. 602611. DOI: 10.106/j.jcp.2011.09.023.
Stinis P. 2012. “MoriZwanzig reduced models for uncertainty quantification II: Initial condition uncertainty.” arXiv:1212.6360v1.
Stinis P. 2012. “MoriZwanzig reduced models for uncertainty quantification I: Parametric uncertainty.” arXiv:1211.4285v1.
Stinis P. 2012. “Numerical computation of solutions of the critical nonlinear Schrödinger equation after the singularity.” Multiscale Modeling and Simulation 10, pp. 4860. DOI: 10.1137/110831222.
Stinis P. 2012. “Stochastic global optimization as a filtering problem.” Journal of Computational Physics 231, pp. 20022014. DOI: 10.1016/j.jcp.2011.11.019
2011
Stinis P. 2011. “Conditional path sampling of stochastic differential equations by drift relaxation.” Communications in Applied Mathematics and Computational Science, Vol. 6, No 1, pp. 6378. DOI: 10.2140/camcos.2011.6.63.
2009
Stinis P. 2009. “A phase transition approach to detecting singularities of PDEs.” Communications in Applied Mathematics and Computational Science, Vol. 4, No. 1, 217239. DOI: 10.2140/camcos.2009.4.217.
2008
Givon D, P Stinis and J Weare. 2009. “Dimensional reduction for particle filters of systems with timescale separation.” IEEE Transactions on Signal Processing, Vol.57 No.2, pp. 424435. DOI: 10.1109/TSP.2008.2008252.
Stinis P. 2008. “Dimensional reduction approach to shock capturing.” Technical Report  Center for Turbulence Research, Stanford.
2007
Hald OH, P Stinis. 2007. “Optimal prediction and the rate of decay for solutions of the Euler equations in two and three dimensions.” Proceedings of the National Academy of Sciences 104, no 16, pp. 65276532. DOI:10.1073/pnas.0700084104 (Published with a Commentary, same issue of PNAS, pp. 64956499, and featured on the cover).
Stinis P. 2007. “Higher order MoriZwanzig models for the Euler equations.” Multiscale Modeling and Simulation 6, no 3, pp. 741760. DOI: 10.1137/06066504X.
Stinis P. 2007. “Dimensional reduction as a tool for mesh refinement and tracking singularities of PDEs.” Technical Report LBNL62851. arXiv: 0706.2895v1.
2006
Chorin AJ, P Stinis. 2006. “Problem reduction, renormalization and memory.” Communications in Applied Mathematics and Computational Science 1, pp. 127. DOI:10.2140/camcos.2006.1.1.
Stinis P. 2006. “A comparative study of two stochastic mode reduction methods.” Physica D, 213, pp. 197213. DOI: 10.1016/j.physd.2005.11.010.
Stinis P, AJ Chorin. 2006. “Numerical scaling analysis of the smallscale structure in turbulence.” Technical Report LBNL59490. arXiv: math/0509027v1.
2005
Stinis P. 2005. “A maximum likelihood algorithm for the estimation and renormalization of exponential densities.” Journal of Computational Physics, 208, pp. 691703. DOI: 10.1016/j.jcp.2005.03.001.
2004
Stinis P. 2004. “Stochastic optimal prediction for the KuramotoSivashinsky equation.” Multiscale Modeling and Simulation 2, no 4, pp. 580612. DOI: 10.1137/030600424.
2003
Stinis P. 2003. “A hybrid method for the inviscid Burgers equation.” Discrete and Continuous Dynamical Systems 9, pp. 793799. DOI:10.3934/dcds.2003.9.793.
Stinis P. 2003. “Stochastic Optimal Prediction for the KuramotoSivashinsky Equation.” PhD Thesis, Columbia University, New York.