Emil M.
Constantinescu
Computational Mathematician
emconsta[at]mcs.anl.gov
Office #: 2151
• LANS
• Mathematics and Computer Science
• Argonne National Laboratory

• Scientist at large at CASE
• University of Chicago

• a Computational Mathematician in the Mathematics and Computer Science Division and part of the Laboratory for Applied Mathematics, Numerical Software, and Statistics at Argonne National Laboratory and
• a Scientist at Large at the Consortium for Advanced Science and Engineering (CASE), The University of Chicago.

• I was a fellow of the Computation Institute, The University of Chicago,
• I was the Wilkinson Fellow in the Mathematics and Computer Science Division, Argonne National Laboratory, and
• I completed my PhD in Computer Science (2008) from Virginia Tech (research highlight).

I am a member of:

• SIAM SISC associate editorial board,
• Scientific committee member at Institute for Mathematical and Statistical Innovation (IMSI)

Recent events:

• Workshop on Statistics Topics in Multiscale, Forecast Verification, and Data Assimilation June 7-10, 2021.


• Workshop on Computational and Mathematical Challenges in Complex Engineering Systems, June 23-24, 2021.

Cool research highlights from around the lab:

Research Interests:

Time-stepping for PDEs and ODEs Multirate - GLM - IMEX Implementation in PETSc library	Uncertainty Quantification and Machine Learning, Data Assimilation & Sensitivity Analysis	Modeling and Simulation
	Forecast page

Projects - active:

Projects - retired:

Upcoming talks/conference presentations:

• at SIAM Computational Science and Engineering (CS&E) (SIAM CSE23), Amsterdam, Feb 26 - Mar 3, 2023.

   

Software:

• DAPack (data assimilation package): A python based data assimilation package.
  Git repository link
  Documentation link

• Contributor to PETSc time stepping algorithms.
  

Recent papers (see all):

• --> Shinhoo Kang and Emil M. Constantinescu, "Learning subgrid-scale models with neural ordinary differential equations." Computers and Fluids, In Press, Vol. 261, Pages 105919, DOI: 10.1016/j.compfluid.2023.105919. [https://arxiv.org/abs/2212.09967]

   

• Johann Rudi, Max Heldman, Emil M. Constantinescu, Qi Tang, and Xian-Zhu Tang, "Scalable implicit solvers with dynamic mesh adaptation for a relativistic drift-kinetic Fokker-Planck-Boltzmann model." Submitted, 2023. [https://arxiv.org/abs/2303.17019]

   

• Daniel Adrian Maldonado, Emil M. Constantinescu, Junbo Zhao, and Mihai Anitescu, "Computationally efficient power system maximum transient linear growth estimation." Submitted, 2023. [https://arxiv.org/abs/2302.10388]

   

• Youngdae Kim, Debojyoti Ghosh, Emil Constantinescu, and Ramesh Balakrishnan, "GPU-Accelerated WENO schemes for the DNS of compressible turbulent flows." Computers & Fluids, Vol 251, Pages 105744, 2023. (DOI: 10.1016/j.compfluid.2022.105744). [https://arxiv.org/abs/2211.16718]

   

• Hong Zhang and Emil M. Constantinescu, "Optimal checkpointing for adjoint multistage time-stepping schemes." Journal of Computational Science, Vol 366, 101913, (DOI: 10.1016/j.jocs.2022.101913). [https://arxiv.org/abs/2106.13879]

   

• Sungho Shin, François Pacaud, Emil Constantinescu, and Mihai Anitescu, "Constrained Policy Optimization for Stochastic Optimal Control under Nonstationary Uncertainties." Advances in Automotive Control , 2022. [https://arxiv.org/abs/2209.13050]

   

• Adrian Maldonado, Emil M. Constantinescu, Hong Zhang, Vishwas Rao, and Mihai Anitescu, "Trust-region approximation of extreme trajectories in power system dynamics." IEEE Transactions on Power Systems , Vol 37(5), Pages 3937-3946, 2022. [https://arxiv.org/abs/2106.16132]

   

• Shinhoo Kang and Emil M. Constantinescu, "Entropy-preserving and entropy-stable relaxation IMEX and multirate time-stepping methods." Journal of Scientific Computing, Vol. 93(23), DOI: 10.1007/s10915-022-01982-w, 2022. [https://arxiv.org/abs/2108.08908]

   

• Hong Zhang, Zhengyu Liu, Emil M. Constantinescu, and Robert Jacob "Stability Analysis of Coupled Advection-Diffusion Models with Bulk Interface Condition" Journal of Scientific Computing , Vol 93(33), DOI: 10.1007/s10915-022-01983-9, 2022.

   

• Ahmed Attia and Emil M. Constantinescu, "Optimal experimental design for inverse problems in the presence of observation correlations. " SIAM Journal on Scientific Computing (SISC) Vol. 44 (4), Pages A2808-A2842, DOI: 10.1137/21M1418666, 2022. [https://arxiv.org/abs/2007.14476]

   

• Alina Kononov, Cheng-Wei Lee, Tatiane Pereira dos Santos, Brian Robinson, Yifan Yao, Yi Yao, Xavier Andrade, Andrew David Baczewski, Emil Constantinescu, Alfredo Correa, Yosuke Kanai, Norman Modine, and Andre Schleife, "Electron dynamics in extended systems within real-time time-dependent density functional theory, " MRS Communications, Pages 1-13, DOI: 10.1557/s43579-022-00273-7, 2022. [https://arxiv.org/abs/2205.04386]

   

• Luisa D'amore, Emil Constantinescu, and Luisa Carracciuolo, "A scalable space-time domain decomposition approach for solving large scale non linear regularized inverse ill posed problems in 4D Variational Data Assimilation, " 91(59) Springer Journal of Scientific Computing, 2022.

   

• Shinhoo Kang, Alp Dener, Aidan Hamilton, Hong Zhang, Emil M. Constantinescu, and Robert Jacob, "Multirate Partitioned Runge-Kutta Methods for Coupled Navier-Stokes Equations." , Submitted, 2022. [https://arxiv.org/abs/2202.11890]

   

• Emil M. Constantinescu, "Implicit extensions of an explicit multirate Runge-Kutta scheme." Applied Mathematics Letters, , Vol 128, Pages 107871, DOI: https://doi.org/10.1016/j.aml.2021.107871, 2022.

   

• Romit Maulik Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, Rao Kotamarthi, "AIEADA 1.0: Efficient high-dimensional variational data assimilation with machine-learned reduced-order models, " GMDD, In press, 2022.

   

• Hong Zhang and Emil M. Constantinescu, "Revolve-Based Adjoint Checkpointing for Multistage Time Integration." ; ICCS 2021 (International Conference on Computational Science 2021), In press.

   

• Hong Zhang, Emil M. Constantinescu, and Barry F. Smith, "PETSc TSAdjoint: a discrete adjoint ODE solver for first-order and second-order sensitivity analysis." SIAM Journal on Scientific Computing, Vol. 44(1), pp C1-C24, 2022. [https://arxiv.org/abs/1912.07696]

   

• Shinhoo Kang, Emil M. Constantinescu, Hong Zhang, and Robert Jacob, "Mass-Conserving Implicit-Explicit Methods for Coupled Compressible Navier-Stokes Equations." Computer Methods in Applied Mechanics and Engineering (CMAME), Vol 384, pp. 113988, 2021, (DOI: 10.1016/j.cma.2021.113988). [https://arxiv.org/abs/2101.09263]

   

• Shaohui Liu, Adrian Maldonado, and Emil Constantinescu, "Probabilistic analysis of masked loads with aggregated photovoltaic production." Electric Power Systems Research, Vol 189, pp. 106670, 2020. [https://arxiv.org/abs/2004.10334]

   

• Emil M. Constantinescu, Noemi Petra, Julie Bessac, and Cosmin G. Petra, "Statistical treatment of inverse problems constrained by differential equations-based models with stochastic terms." SIAM Journal on Uncertainty Quantification, Vol 8(1), pp. 170-197, 2020. [https://arxiv.org/abs/1810.08557]

   

• Vishwas Hebbur Venkata Subba Rao, Romit Maulik, Emil Constantinescu, and Mihai Anitescu, "A machine learning method for computing rare event probabilities." International Conference on Computational Science (ICCS), Submitted; 3-5 June, 2020. [https://arxiv.org/abs/2006.03466]

   

• Hong Zhang, Zhengyu Liu, Emil M. Constantinescu, and Robert Jacob, "Stability analysis of interface conditions for ocean-atmosphere coupling." Springer Journal of Scientific Computing, Vol. 84 (44), 2020. [https://arxiv.org/abs/1909.00916]

   

• Joseph Hart*, Julie Bessac, and Emil M. Constantinescu, "Global sensitivity analysis for statistical model parameters." SIAM/ASA Journal on Uncertainty Quantification, 7(1), 67–92, 2019. [Preprint # ANL/MCS-P8006-0817, https://arxiv.org/abs/1708.07441].

   

• Ahmed Attia* and Emil M. Constantinescu, "An optimal experimental design framework for adaptive inflation and covariance localization for ensemble filters." Submitted, 2018. [https://arxiv.org/abs/1806.10655]

   

• Valeria Mele, Emil M. Constantinescu, Luisa Carracciuolo, and Luisa D'Amore, "A PETSc parallel-in-time solver based on MGRIT algorithm." Submitted, Concurrency and Computation: Practice and Experience, 2018.

   

• Hanqi Guo, Wenbin He, Sangmin Seo, Han-Wei Shen, Emil M. Constantinescu, Chunhui Liu, and Tom Peterka, "Extreme-scale stochastic particle tracing for uncertain unsteady flow visualization and analysis." IEEE Transactions on Visualization and Computer Graphics, Vol 25(9), Pages 2710-2724, 2019.

   

• Jiali Wang, Julie Bessac, Rao Kotamarthi, Emil M. Constantinescu, and Beth Drewniak, "Internal variability, regional climate model, spectral nudging, high spatial resolution, climate change." Climate Dynamics, Vol 50(11-12), Pages 4539-4559, 2018.

   

• Oana Marin*, Emil Constantinescu, and Barry Smith, "PDE-constrained optimization with spectral elements using PETSc and TAO." Submitted, 2018. [https://arxiv.org/abs/1806.01422]

   

• Shrirang Abhyankar, Jed Brown, Emil M. Constantinescu, Debojyoti Ghosh*, Barry F. Smith and Hong Zhang*, "PETSc/TS: A modern scalable ODE/DAE solver library." Submitted, 2018. [https://arxiv.org/abs/1806.01437]

   

• Julie Bessac*, Emil M. Constantinescu, and Mihai Anitescu, "Stochastic simulation of predictive space-time scenarios of wind speed using observations and physical models." Vol 12(1), Pages 432-458, Annals of Applied Statistics, 2018. [Preprint # ANL/MCS-P5432-1015, http://arxiv.org/abs/1511.09416].

   

• Simone Marras, Michal A. Kopera, Emil M. Constantinescu, Jenny Suckale, and Francis X. Giraldo "A residual-based shock capturing scheme for the continuous/discontinuous spectral element solution of the 2D shallow water equations." Vol. 114, Pages 45-63, Advances in Water Resources, 2018. [http://arxiv.org/abs/1607.04547]

   

• Emil Constantinescu, "Generalizing global error estimation for ordinary differential equations by using coupled time-stepping methods." Journal of Computational and Applied Mathematics, Vol 332(C), Pages 140-158, 2018. (https://arxiv.org/abs/1503.05166)

   

updated August 2022