Skip to Main Content U.S. Department of Energy
Fundamental and Computational Sciences Directorate

Staff information

Panagiotis Stinis

Computational Mathematics

PNNL Publications


  • 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
  • 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


  • 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
  • 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/

Science at PNNL

Core Research Areas

User Facilities

Centers & Institutes

Research Highlights

View All Research Highlights & Staff Accomplishments

RSS Feed