Publications

Thorpe, J., Musuvathy, S., Verzi, S.J., Vugrin, E., Dykstra, M., Sahakian, M., & Sahakian, M. (2021). GAMVT: A Generative Algorithm for MultiVariate Timeseries Data [Conference Paper]. https://www.osti.gov/biblio/1886761

Rodriguez, J., Parker, R., Laird, C., Nicholson, B.L., Siirola, J.D., Bynum, M., & Bynum, M. (2021). Scalable Parallel Nonlinear Optimization with PyNumero and Parapint. Optimization Online Repository. https://www.osti.gov/biblio/1834337

Rackers, J.A., Lee, A., & Lee, A. (2021). Machine learning to build quantum-accurate models for biological macromolecules [Presentation]. https://www.osti.gov/biblio/1886174

Oldfield, R., Plimpton, S.J., Bachman, W.B., Poliakoff, D., Sornborger, A., & Sornborger, A. (2021). Memo regarding the Final Review of FY21 ASC L2 Milestone 7840: Neural Mini-Apps for Future Heterogeneous HPC Systems. https://doi.org/10.2172/1825628

Abdelfattah, A., Anzt, H., Ayala, A., Boman, E.G., Carson, E.C., Cayrols, S., Cojean, T., Dongarra, J.J., Falgout, R., Gates, M., G, R., Higham, N.J., Kruger, S.E., Li, S., Lindquist, N., Liu, Y., Loe, J.A., Nayak, P., Osei-Kuffuor, D., … Yang, U.M. (2021). Advances in Mixed Precision Algorithms: 2021 Edition. https://doi.org/10.2172/1814447

Gorodetsky, A.A., Jakeman, J.D., Geraci, G., & Geraci, G. (2021). MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources. Computational Mechanics, 68(4), pp. 741-758. https://doi.org/10.1007/s00466-021-02042-0

Swiler, L.P., Basurto, E., Brooks, D.M., Eckert, A., Leone, R.C., Mariner, P.E., Portone, T., Bachman, W.B., Stein, E., & Stein, E. (2021). Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2021). https://doi.org/10.2172/1855018

Merrell, D.P., Robinson, A.C., Sanchez, J.J., & Sanchez, J.J. (2021). Statistical Distributions for Mesh Independent Solutions in ALEGRA. https://doi.org/10.2172/1813648

Tarman, T.D., Swiler, L.P., Vugrin, E., Rollins, T., Cruz, G.J., Huang, H., Sahu, A., Wlazlo, P., Goulart, A., Davis, K., & Davis, K. (2021). Comparing reproduced cyber experimentation studies across different emulation testbeds [Conference Presenation]. ACM International Conference Proceeding Series. https://doi.org/10.2172/1881645

Swiler, L.P., Brooks, D.M., Stein, E., Rohlig, K., Plischke, E., Becker, D., Spiessl, S.M., Koskinen, L., Govaerts, J., Svitelman, V., Elena, S., & Elena, S. (2021). Metamodelling sensitivity approaches versus regression and graphical methods on the basis of Geologic Cases: An International Collaboration [Conference Presenation]. https://doi.org/10.2172/1884666

Rackers, J.A. (2021). What is Machine Learning good for anyway? [Presentation]. https://www.osti.gov/biblio/1881684

Tranchida, J. (2021). Magneto-elastic predictions with LAMMPS and the SPIN package [Conference Presenation]. https://doi.org/10.2172/1884091

Ibanez-Granados, D.A. (2021). Better Package Management [Presentation]. https://www.osti.gov/biblio/1883461

Acer, S., Boman, E.G., Glusa, C., Rajamanickam, S., & Rajamanickam, S. (2021). Sphynx: a parallel multi-GPU graph partitioner [Conference Presenation]. https://doi.org/10.2172/1882077

Cyr, E.C., Guenther, S., Ruthotto, L., Schroder, J.B., Gauger, N.R., Moon, G., Patel, R., & Patel, R. (2021). A Layer-Parallel Approach for Training Deep Neural Networks [Conference Presenation]. https://doi.org/10.2172/1881678

Moore, S.G. (2021). The State of the LAMMPS KOKKOS Package [Conference Presenation]. https://doi.org/10.2172/1888676

Raybourn, E.M., Watson, G., Gonsiorowski, E., Milewicz, R.M., Rogers, D.M., Sims, B., Willenbring, J.M., & Willenbring, J.M. (2021). Automating Software Productivity Planning: Lightweight Tools for Upgrading Team Practices [Conference Proceeding]. https://www.osti.gov/biblio/1884442

Harvey-Thompson, A.J., Geissel, M., Crabtree, J.A., Weis, M.R., Gomez, M.R., Fein, J.R., Ampleford, D.J., Awe, T.J., Chandler, G.A., Galloway, B.R., Hansen, S.B., Hanson, J., Harding, E.C., Jennings, C.A., Kimmel, M., Knapp, P.F., Lamppa, D.C., Bachman, W.B., Mangan, M.A., … Smith, G.E. (2021). Increased preheat energy to MagLIF targets with cryogenic cooling [Conference Presenation]. https://doi.org/10.2172/1888953

Voth, T.E., Granzow, B.N., Stagg, A.K., Bond, S.D., Hamlin, N.D., Martin, M.R., Shulenburger, L., & Shulenburger, L. (2021). FLEXO: Development of a Portably Performant AMR Code for XMHD [Conference Presenation]. https://doi.org/10.2172/1881674

Eldred, C., Gay-Balmaz, F., & Gay-Balmaz, F. (2021). Thermodynamically consistent semi-compressible fluids: A variational perspective. Journal of Physics A: Mathematical and Theoretical, 54(34). https://doi.org/10.1088/1751-8121/ac1384

Jacobson, N.T., Albrecht, D., & Albrecht, D. (2021). NoMoPy: Noise Modeling in Python [Conference Presenation]. https://doi.org/10.2172/1882098

Hartland, T., Perego, M., Petra, N., & Petra, N. (2021). Inversion of the Humboldt Glacier Basal Friction Coefficient Field in an Uncertain Ice Sheet Model [Presentation]. https://www.osti.gov/biblio/1884051

Plimpton, S.J. (2021). How to modify LAMMPS [Conference Presenation]. https://doi.org/10.2172/1888427

Raybourn, E.M., Milewicz, R.M., Rogers, D.M., Sims, B., Watson, G., Gonsiorowski, E., Willenbring, J.M., & Willenbring, J.M. (2021). A Data-driven Approach to Rethinking Open Source Software Organizations as a Team of Teams [Conference Proceeding]. https://www.osti.gov/biblio/1881691

Jacobson, N.T., Young, S.M., & Young, S.M. (2021). Sandia/Princeton collaboration: Multivalley effective mass theory modeling of Princeton SiGe devices [Conference Presenation]. https://doi.org/10.2172/1882099

Loe, J.A. (2021). An Introduction to Trilinos [Presentation]. https://www.osti.gov/biblio/1884905

Poliakoff, D. (2021). Kokkos Tools [Presentation]. https://www.osti.gov/biblio/1888126

Rackers, J.A. (2021). What can machine learning teach us about the limits of electron correlation? [Conference Presenation]. https://doi.org/10.2172/1884653

Lewis, C., Phipps, E.T., & Phipps, E.T. (2021). Low-Communication Asynchronous Distributed Generalized Canonical Polyadic Tensor Decomposition [Conference Paper]. https://doi.org/10.1109/HPEC49654.2021.9622844

Silling, S.A. (2021). Calibrating a Peridynamic Material Model with Molecular Dynamics [Conference Presenation]. https://doi.org/10.2172/1883458

Magann, A.B. (2021). Quantum computing: where we are now, & where we're headed [Presentation]. https://www.osti.gov/biblio/1884177

Sahakian, M., Musuvathy, S., Thorpe, J., Verzi, S.J., Vugrin, E., Dykstra, M., & Dykstra, M. (2021). Threat data generation for space systems [Conference Presenation]. Proceedings - 2021 IEEE Space Computing Conference, SCC 2021. https://doi.org/10.2172/1873278

Gastelum, Z.N., Shead, T.M., Rushdi, A., & Rushdi, A. (2021). A Safeguards-Informed Image Dataset for Computer Vision R&D [Conference Presenation]. https://doi.org/10.2172/1888462

Wood, M.A. (2021). LAMMPS Users Meeting 2021: Visualization Tutorial [Conference Presenation]. https://doi.org/10.2172/1891069

Magann, A.B. (2021). Designing quantum controls efficiently [Presentation]. https://www.osti.gov/biblio/1890882

Shead, T.M., Gastelum, Z.N., Rushdi, A., & Rushdi, A. (2021). Synthetic Images for Machine Learning [Presentation]. https://www.osti.gov/biblio/1881667

Bachman, W.B., Schleife, A., Kononov, A., Hansen, S.B., Baczewski, A.D., & Baczewski, A.D. (2021). Optical Properties of Be at High Temperatures from First Principles [Presentation]. https://www.osti.gov/biblio/1884067

Wood, M.A., Thompson, A.P., Cusentino, M.A., Montes de Zapiain, D., Oleynik, I., & Oleynik, I. (2021). Interatomic Potentials for Materials Science and Beyond; Advances in Machine Learned Spectral Neighborhood Analysis Potentials [Conference Presenation]. https://doi.org/10.2172/1883516

Xiao, T.P., Feinberg, B., Bennett, C., Agarwal, S., Marinella, M., & Marinella, M. (2021). Designing Accurate and Robust Analog Accelerators for Neural Networks and Linear Algebra [Presentation]. https://www.osti.gov/biblio/1882297

Tranchida, J. (2021). Large-scale theroretical predictions of pressure and temperature ramps in iron [Conference Presenation]. https://doi.org/10.2172/1888143

Swan, M.S., Sirajuddin, D., Cartwright, K.L., Moore, C.H., & Moore, C.H. (2021). A User Perspective on HPC Challenges and Diagnostics [Presentation]. https://www.osti.gov/biblio/1890903

Szalek, N., Gardelle, J., Grua, P., Hebert, D., Moore, C.H., Medina, B., Swan, M.S., Cartwright, K.L., & Cartwright, K.L. (2021). EXPERIMENTAL STUDIES OF ELECTRON BEAM PROPAGATION IN ARGON [Conference Presenation]. https://doi.org/10.2172/1883493

Loe, J.A. (2021). Using Multiple Precisions in the GMRES Linear Solver [Presentation]. https://www.osti.gov/biblio/1884904

Hart, J.L., van Bloemen Waanders, B.G., Saibaba, A.K., & Saibaba, A.K. (2021). Randomized algorithms for generalized singular value decomposition with application to sensitivity analysis. Numerical Linear Algebra with Applications, 28(4). https://doi.org/10.1002/nla.2364

D'Elia, M., Silling, S.A., Yu, Y., You, H., & You, H. (2021). A data-driven peridynamic continuum model for upscaling molecular dynamics. https://doi.org/10.2172/1821529

Moore, S.G. (2021). Overview of Molecular Dynamics [Conference Presenation]. https://doi.org/10.2172/1882348

Cusentino, M.A., Wood, M.A., Thompson, A.P., & Thompson, A.P. (2021). Development of SNAP Potentials for Fusion Reactor Materials [Conference Presenation]. https://doi.org/10.2172/1882069

Geronimo Anderson, S.I., Teranishi, K., Dunlavy, D.M., Choi, J., & Choi, J. (2021). Performance-Portable Sparse Tensor Decomposition Kernels on Emerging Parallel Architectures [Conference Paper]. https://www.osti.gov/biblio/1884665

di Matteo, O., Gamble, J., Granade, C., Rudinger, K.M., Wiebe, N., & Wiebe, N. (2021). Operational Quantum Tomography [Conference Presenation]. https://doi.org/10.2172/1882132

Ahmed, H., Lofstead, G.F., & Lofstead, G.F. (2021). Quantifying the Crisis in Reproducible Machine Learning [Conference Poster]. https://doi.org/10.2172/1883039

Rackers, J.A. (2021). What can you do with a polarizable force field? [Conference Presenation]. https://doi.org/10.2172/1884423

Berry, J., Phillips, C.A., Porter, A., & Porter, A. (2021). Maintaining Connected Components in Infinite Graph Streams [Presentation]. https://doi.org/10.1145/2501221.2501234

Trask, N.A. (2021). A data-driven exterior calculus for model discovery [Conference Presenation]. https://doi.org/10.2172/1882073

Huang, A., Gao, X., Reza, S., Trask, N.A., Wilcox, I.Z., Diaz, C.P., Patel, R., & Patel, R. (2021). Machine learning surrogates of high-fidelity electrical models [Presentation]. https://www.osti.gov/biblio/1882298

Sahakian, M., Musuvathy, S., Thorpe, J., Verzi, S.J., Vugrin, E., Dykstra, M., & Dykstra, M. (2021). Threat data generation for space systems [Conference Paper]. Proceedings - 2021 IEEE Space Computing Conference, SCC 2021. https://doi.org/10.1109/SCC49971.2021.00018

Laros, J.H., Hammond, S., Bachman, W.B., & Bachman, W.B. (2021). Vanguard-II Application Evaluation Thrust [Presentation]. https://www.osti.gov/biblio/1884443

Smith, C., Albash, T., Campbell, Q.T., Baczewski, A.D., & Baczewski, A.D. (2021). Neural Network Quantum Simulation [Conference Presenation]. https://doi.org/10.2172/1889560

Abubaker, N., Acer, S., Aykanat, C., & Aykanat, C. (2021). True Load Balancing for Matricized Tensor Times Khatri-Rao Product. IEEE Transactions on Parallel and Distributed Systems, 32(8), pp. 1974-1986. https://doi.org/10.1109/tpds.2021.3053836

Gao, X., Mendez Granado, J.P., Lu, T., Anderson, E.M., de Campbell, A., Ivie, J.A., Schmucker, S.W., Grine, A., Lu, P., Tracy, L.A., Arghavani, R., Misra, S., & Misra, S. (2021). Modeling and Assessment of Atomic Precision Advanced Manufacturing (APAM) Enabled Vertical Tunneling Field Effect Transistor [Conference Paper]. https://doi.org/10.1109/SISPAD54002.2021.9592578

Aitken, R., Nakahira, Y., Strachan, J.P., Bresniker, K., Young, I., Li, Z., Klebanoff, L.E., Burchard, C., Kumar, S., Marinella, M., Severa, W., Talin, A.A., Vineyard, C.M., Mailhiot, C., Dick, R., Lu, W., Mogill, J., & Mogill, J. (2021). Energy Efficient Computing R&D Roadmap Outline for Automated Vehicles. https://doi.org/10.2172/1821804

Chance, F.S. (2021). Lessons from α Dragon Fly's Brain: Evolution Built a Small, Fast, Efficient Neural Network in a Dragonfly. Why Not Copy It for Missile Defense?. IEEE Spectrum, 58(8), pp. 28-33. https://doi.org/10.1109/mspec.2021.9502906

Nichol, J.J. (2021). Causal Inference and Causal Discovery [Presentation]. https://www.osti.gov/biblio/1882126

Rackers, J.A. (2021). What can machine learning and the Hellmann-Feynman Theorem teach us about the limits of electron correlation? [Conference Presenation]. https://doi.org/10.2172/1884141

Leonard, F., Backer, A.S., Fuller, E.J., Teeter, C.M., Vineyard, C.M., & Vineyard, C.M. (2021). Co-Design of Free-Space Metasurface Optical Neuromorphic Classifiers for High Performance. ACS Photonics, 8(7), pp. 2103-2111. https://doi.org/10.1021/acsphotonics.1c00526

Trott, C.R., Lebrun-Grandie, D., Arndt, D., Ciesko, J., Dang, V., Ellingwood, N.D., Gayatri, R., Harvey, E.C., Hollman, D.S., Ibanez-Granados, D.A., Liber, N., Madsen, J., Miles, J.S., Poliakoff, D., Powell, A.J., Rajamanickam, S., Simberg, M., Sunderland, D., Turcksin, B., Wilke, J., & Wilke, J. (2021). Kokkos 3: Programming Model Extensions for the Exascale Era. IEEE Transactions on Parallel and Distributed Systems, 33(4), pp. 805-817. https://doi.org/10.1109/TPDS.2021.3097283

Jakeman, J.D., Kouri, D.P., Huerta, J.G., & Huerta, J.G. (2021). Surrogate Modeling For Efficiently Accurately and Conservatively Estimating Measures of Risk. https://doi.org/10.2172/1807455

Friedman, S., Jakeman, J.D., Eldred, M., Tamellini, L., Gorodestky, A.A., Allaire, D., & Allaire, D. (2021). Adaptive resource allocation for surrogate modeling of systems comprised of multiple disciplines with varying fidelity. https://doi.org/10.2172/1807453

Rempe, S.B., Jing, Z., Rackers, J.A., Pratt, L.R., Liu, C., Ren, P., & Ren, P. (2021). Thermodynamics of ion binding and occupancy in potassium channels. Chemical Science, 12(25), pp. 8920-8930. https://doi.org/10.1039/d1sc01887f

Aimone, J.B., Ho, Y., Parekh, O., Phillips, C.A., Pinar, A., Severa, W., Wang, Y., & Wang, Y. (2021). Provable advantages for graph algorithms in spiking neural networks [Conference Paper]. Annual ACM Symposium on Parallelism in Algorithms and Architectures. https://doi.org/10.1145/3409964.3461813

dos Santos, G., Meyer, R., Aparicio, R., Tranchida, J., Bringa, E.M., Urbassek, H.M., & Urbassek, H.M. (2021). Spin-lattice dynamics of surface vs core magnetization in Fe nanoparticles. Applied Physics Letters, 119(1). https://doi.org/10.1063/5.0055606

Hart, W.E., Laird, C., Rodriguez, J.S., Siirola, J.D., Nicholson, B.L., & Nicholson, B.L. (2021). Rethinking the C++ / Python Boundary in Modeling and Optimization Tools [Presentation]. https://www.osti.gov/biblio/1882366

Frink, C. (2021). Benchmarking Quantum Algorithms with Neural Networks [Presentation]. https://www.osti.gov/biblio/1884402

Miller, N., Hughes, C., & Hughes, C. (2021). Comparing Intel Compilers Poster [Presentation]. https://www.osti.gov/biblio/1882504

Torchinsky, J., Taylor, M.A., & Taylor, M.A. (2021). Improved vertical remapping accuracy [Presentation]. https://www.osti.gov/biblio/1883500

Kouri, D.P. (2021). An Inexact Trust-Region Newton Method for Large-Scale Convex-Constrained Optimization [Conference Presenation]. https://doi.org/10.2172/1878281

Lennon, K., Rajamanickam, S., & Rajamanickam, S. (2021). Learning Transferable DFT Neural Network Surrogates [Presentation]. https://www.osti.gov/biblio/1884431

Younge, A.J., Fuller, T.J., Bachman, W.B., Bova, S.W., & Bova, S.W. (2021). A Case Study in Using Containers to Build and Distribute HPC Applications: ALEGRA [Conference Presenation]. https://doi.org/10.2172/1876620

Laird, C., Nicholson, B.L., Rodriguez, J.S., & Rodriguez, J.S. (2021). Parallel strategies for DAE optimization with direct Schur-complement decomposition [Conference Poster]. https://www.osti.gov/biblio/1883541

Moore, N. (2021). Learning Algebraic Multigrid Prolongation with Residual Graph Neural Networks [Presentation]. https://www.osti.gov/biblio/1877980

Lofstead, G.F. (2021). Simulating the Impossible via Breaking Assumptions [Conference Presenation]. https://doi.org/10.2172/1876622

Gilman, K., Phipps, E.T., Kolla, H., & Kolla, H. (2021). Split Bregman optimizer for online generalized CP tensor decomposition [Presentation]. https://www.osti.gov/biblio/1881725

McCrary, T., Devine, K., & Devine, K. (2021). Integrating PGAS and MPI-Based Graph Analysis [Presentation]. https://www.osti.gov/biblio/1881669

Worley, A., Schafer, D., Bangalore, P.V., Dosanjh, M.G.F., Grant, R., Skjellum, A., Ghafoor, G., & Ghafoor, G. (2021). Design of a Portable Implementation of Partitioned Point-to-Point Communication Primitives [Conference Paper]. https://doi.org/10.1145/3458744.3474046

Bynum, M., Rodriguez, S., Laird, C., Nicholson, B.L., Jalving, J.H., Siirola, J.D., Ridzal, D., & Ridzal, D. (2021). Scalable Parallel Nonlinear Optimization with PyNumero and Parapint [Conference Presenation]. https://doi.org/10.2172/1884206

Jakeman, J.D., Eldred, M., Geraci, G., Portone, T., Rushdi, A., Seidl, D.T., Smith, T.M., & Smith, T.M. (2021). Multi-fidelity Machine Learning [Conference Presenation]. https://doi.org/10.2172/1876608

Nichol, J.J., Peterson, M.G., Fricke, M., & Fricke, M. (2021). Learning Why: Data-Driven Causal Evaluations of Climate Models [Conference Proceeding]. https://www.osti.gov/biblio/1888471

Hardesty, S., Antil, H., Kouri, D.P., Ridzal, D., & Ridzal, D. (2021). The Strip Method for Shape Derivatives [Conference Presenation]. https://doi.org/10.2172/1876615

Cyr, E.C. (2021). Perfection is the Enemy of Fast: The Case for Mathematically Induced Parallelism [Presentation]. https://www.osti.gov/biblio/1883495

Portone, T., Bachman, W.B., Dandekar, R., Rackauckas, C., & Rackauckas, C. (2021). Learning Missing Mechanisms in a Dynamical System from a Subset of State Variable Observations [Conference Presenation]. https://doi.org/10.2172/1889367

Hu, J.J., Glusa, C., & Glusa, C. (2021). Recent Advances in Maxwell Solvers for PIC Simulations Across Architectures [Conference Presenation]. https://doi.org/10.2172/1888437

Logan, L.M., Lofstead, G.F., & Lofstead, G.F. (2021). pMEMCPY: an efficient I/O library for PMEM [Conference Poster]. https://doi.org/10.2172/1888709

Laird, C., Watson, J., Hart, W.E., Siirola, J.D., Nicholson, B.L., & Nicholson, B.L. (2021). Pyomo Tutorial [Presentation]. https://www.osti.gov/biblio/1882354

McQuarrie, S., van Bloemen Waanders, B.G., & van Bloemen Waanders, B.G. (2021). Goal-oriented data-driven reduced-order modeling guided by hyper-differential sensitivity analysis [Presentation]. https://www.osti.gov/biblio/1884436

Mayer, C.D. (2021). Use My Data, But Don't Make Me Share It: Hybrid Secure MultiParty Computation for Privacy-Preserving Machine Learning [Presentation]. https://www.osti.gov/biblio/1882290

Loe, J.A., Glusa, C., Yamazaki, I., Boman, E.G., Rajamanickam, S., & Rajamanickam, S. (2021). Properties of GMRES with Iterative Refinement on GPUs [Conference Presenation]. https://doi.org/10.2172/1884157

Maniaci, D.C., Naughton, J., Geraci, G., King, R., & King, R. (2021). Verification, Validation & Uncertainty Quantification [Presentation]. https://www.osti.gov/biblio/1883505

Low, M.R. (2021). Trajectory Anomaly Detection [Presentation]. https://www.osti.gov/biblio/1884130

Crockatt, M.M., Shadid, J.N., Conde, S., Pawlowski, R., Mabuza, S., & Mabuza, S. (2021). Development of an Algorithmic Framework for the Simulation of Partially-Ionized Multifluid Plasma Systems with Arbitrarily Many Species [Conference Presenation]. https://doi.org/10.2172/1884180

Crossno, P.J., Gittinger, J., Hunt, W.L., Letter, M., Martin, S., Sielicki, M., & Sielicki, M. (2021). Slycat? Ensemble Analytics [Presentation]. https://www.osti.gov/biblio/1882478

Milewicz, R.M., Pirkelbauer, P., Soundarajan, P., Ahmed, H., Skjellum, T., & Skjellum, T. (2021). Negative Perceptions About the Applicability of Source-to-Source Compilers in HPC: A Literature Review [Conference Presenation]. https://doi.org/10.2172/1884057