Computer Science Research Institute Summer Proceedings 2021

J. Darby Smith and Edgar Galvan, editors, Computer Science Research Institute Summer Proceedings 2021, The Computer Science Research Institute at Sandia National Laboratories, Albuquerque, NM, 2021. Available as Sandia National Laboratories Technical Report SAND2022-0653R.

Individual Articles

• N.E. Buczkowski, M.D. Foss, M.L. Parks, J. Trageser, and P. Radu, Two Nonlocal Biharmonic Operators, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E.Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 3–15.
• N.J. Christensen and E.J. Parish, A Parallel-in-Time Approach to Solving the 1D Advection Equation Using the Euler-Lagrange Equations, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E.Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 16–23.
• A. de Castro, P. Kuberry, and P. Bochev, Partitioned Solution of a Coupled Reduced Order Model – Finite Element Model (ROM-FEM model) for a Transmission Problem, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 24–37.
• T.Z. Dean and E. Galvan, A Survey of the Koopman Operator and Moment Propagation, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 38–60.
• K. Gilman and E. Phipps, Majorize-Minimize Algorithms for Streaming Generalized Canonical Polyadic Tensor Decompositions, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 61–76.
• R. Jin, F. Rizzi, and E. Parish, Space-time Reduced-order Modeling for Uncertainty Quantification, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 77–89.
• M.F. McCracken and S. Miller, Study of the Recovery Discontinuous Galerkin Method and its Application to EMPIRE, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 90–100.
• S.A. McQuarrie, J. Hart, B. van Bloemen Waanders, and K. Willcox, Data-Driven Model Reduction for Physics-Constrained Optimization, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 101–111.
• H.A. Olson, M. D’Elia, M. Foss, M. Gulian, and P. Radu, Comparison of Tempered and Truncated Fractional Models, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 112–119.
• R. Parker, B. Nicholson, J. Siirola, C. Laird, and L. Biegler, A Reduced-space Formulation for Nonlinear Programming with Index-1 Differential Algebraic Equation Systems, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2020, pp. 120–128.
• L. Partin, G. Geraci, A.A. Rushdi, M.S. Eldred, and D.E. Schiavazzi, Multifidelity Data Fusion in Convolutional Encoder/Decoder Assembly Networks for Computational Fluid Dynamics Applications, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 129–146.
• A. Voronin and R. Tuminaro, Algebraic Multigrid based on Low-order Systems, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2020, pp. 147–158.
• J. Fox, N.A. Modine, and S. Rajamanickam, Accelerating Electronic Structure Calculation with Atom-Decomposed Neural Modeling, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 160–169.
• M.S. Gilbert, K. Madduri, E. Boman, and S. Rajamanickam, Fine-Grained Parallel Graph Partition Refinement, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 170–184.
• D.A. Kruse, W.P. Marts, and M.G.F. Dosanjh, An Exploration of MiniMod Overhead and Granularity, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 185–192.
• Z. Li and H. Kolla, Efficient Computation of Higher Order Joint Moment Tensor, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 193–203.
• L.M. Logan and G.F. Lofstead, pMEMCPY: an Efficient I/O Library for PMEM, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 204–214.
• M.R. Low and A.T. Wilson, Geo-spatial Visualizations, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 215–224.
• S. Luca and F. Wang, Periodic Loss Function for Grid Cell Encodings, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 225–234.
• T.M. McCrary, K.D. Devine, and A.J. Younge, Integrating PGAS and MPI-Based Graph Analysis, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 235–249.
• C. Pereyra and M.A. Wood, Towards Viable Use of Machined Learned Models in Physical Simulation, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 250–261.
• C. Woods and M.L. Curry, Verification and Performance Testing of the Advanced Tri-Lab Software Environment, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 262–267.
• T.A. Alsup and T.A. Catanach, Expected Infomation Gain Estimates and Bayesian Optimal Experimental Design, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 269–282
• J.P. Callahan and T.A. Catanach, Importance Sampling in Bayesian OED for Sensor Placement, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 283–292.
• K.B. Clements, G. Geraci, and A.J. Olson, A Variance Deconvolution Approach to Sampling Uncertainty Quantification for Monte Carlo Radiation Transport Solvers, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 293–307.
• C. Frink, Q. Campbell, C. Smith, A.D. Baczewski, and T. Albash, Benchmarking Neural Network Ansätze for Quantum Chemistry Applications, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 308–320.
• M.J. Gaiewski, K.C. Sockwell, J.C. Connors, and P.B. Bochev, Air-Sea Light, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 321–334.
• K.E. Gerot and K.L. DiPietro, Precipitation Model Aggregation using Optimal Transport, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 335–346.
• T. Hartland and M. Perego, Towards Inversion of the Basal Sliding Coefficient for the Humboldt Glacier in an Uncertain Ice Sheet Model, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 347–359.
• J. Hoy, I. Tezaur, and A. Mota, The Schwarz Alternating Method for Multiscale Contact Mechanics, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 360–378.
• K.R. Lennon and S. Rajamanickam, Learning Transferable Neural Network Surrogates for Kohn-Sham Density Functional Theory, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 379–391.
• J.L. Torchinsky and M.A. Taylor, Improved Vertical Remapping Accuracy in the NH-HOMME Atmosphere Dynamical Core, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 392–404.
• S.M. Valaitis and K.A. Maupin, Machine-Learning for Single Particle Motion in Plasmas, in Computer Science Research Institute Summer Proceedings 2021, J.D. Smith and E. Galvan, eds., Technical Report SAND2022-0653R, Sandia National Laboratories, 2021, pp. 405–417.