Computer Science Research Institute Summer Proceedings 2025

Marissa B. P. Adams, Tiernan A. Casey, Juan P. Mendez Granado and K. Skolfield, editors, Computer Science Research Institute Summer Proceedings 2025, The Computer Science Research Institute at Sandia National Laboratories, Albuquerque, NM and Livermore, CA, 2025. Available as Sandia National Laboratories Technical Report SAND2025-14267O.

Individual Articles

• M. S. Ackermann, P. Bochev, and D. Ridzal, Towards a Reduced Order Model for Hyperbolic Partial Differential Equations, Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 3–16.
• B. Baraldi and A. Sethi-Olowin, Mixed Precision Linear Algebra in Trust-Region Methods for PDE Constrained Optimization. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 17–29.
• J. Been, G. Geraci, and A. Javeed, Accounting for Pilot Sampling in Multi-Fidelity Uncertainty Quantification. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 30–43.
• M. W. Brown, J. Chaudhary, and J. Shadid, Simulation of Hyperbolic Partial Differential Equations using a Vector Lattice Boltzmann Framework. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 44–55.
• T. T. Burnett, A. Gruber, and E. C. Cyr, Towards Structure-Preserving Reduced Order Modeling of Particle in Cell Simulations. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 56–69.
• D. Coble and D. Kouri, A Trust-Region Method with Inexact Projection for Polyhedral Constraints. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 70–82.
• M. L. Descoteaux and J. M. Goff, Flexible Charges in LAMMPS with Machine Learned Interatomic Potentials. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 83–95.
• M. L. Dubey, B. R. Hillman, and M. A. Taylor, Scream if you Love -5/3: Kinetic Energy Spectra and HRRR-Based Evaluation of the E3SM Global Storm-Resolving Model. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 96–103.
• J. Haydel Collins, A. C. Robinson, and D. A. Ibanez-Granados, A Shifted Boundary Method Inspired Approach to Elliptic Interface Problems. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 104–111.
• D. Hughes and C. Eldred, Comparison of Hodge Stars in Discrete Exterior Calculus. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 112–120.
• E. Huynh, J. Owen, P. Kuberry, and P. Bochev, A Dynamic Flux Surrogate for Coupling Full-Order with DMD Subdomain Models. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 121–142.
• M. Madhavan, J. Hart, and B. van Bloemen Waanders, Hyper-Differential Sensitivity Analysis With Respect To Model Discrepancy: Sequential Optimal Experimental Design. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 143–160.
• A. Merkley, F. S. Chance, and W. Chapman, Redundant and Synergistic Information Flow in Neural Populations During Navigation. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 161–175.
• J. Mozebo and E. Phipps, GENTEN: Applications of Tensor Decomposition in Data Analysis. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 176–187.
• J. C. Nikolai and C. Eldred, Improved Discretizations for Electromagnetic Simulations. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 188–201.
• K. A. Ohene-Obengand K. Maupin, Functional Data Analysis for Physics Model Calibration with Embedded Uncertainty Quantification. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 202–217.
• Y. Owusu-Agyemang, D. Ridzal, T. Wildey, R. Khandelwal, and H. Antil, Numerical Studies of Optimal Control Problems Governed by the Time-Dependent Maxwell’s Equations. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 218–229.
• C. Rodriguez, I. Tezaur, A. Mota, A. Gruber, E. Parish, and C. Wentland, Transmission Conditions for the Non-Overlapping Schwarz Coupling of Full-Order and Operator Inference Models. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 230–249.
• R. Roy-Chowdhury, I. Tezaur, N. Urban, and A. Gruber, Onsager’s Variational Principle for Solving Inverse Problems. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 250–264.
• P. Smith, R. Bandy, M. Khalil, T. Portone, and K. Neal, Quantifying Uncertainty in Data-Driven Closure Models. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 265–285.
• J. Turnage, M. Lowery, and J. Jakeman, Benchmarking Generative Models for Solving High-Dimensional Goal-Oriented Inverse Problems. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 286–303.
• L. Zichi, A. Thompson, and M. J. McCarthy, Generalized and Scalable Hybrid Monte Carlo Molecular Dynamics Simulations. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 304–314.
• A. Alvey-Blanco, B. Kelley, and S. Rajamanickam, Platform-Agnostic Kernel Fusion Pipeline for Linear Algebra Kernels in MLIR. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 316–327.
• C. Brynteson, R. Milewicz, and J. Wilenbring, Test Generation in a Complex Scientific Computing Environment: A Experience Report. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 328–336.
• D. J. Camacho and J. J. Hu, Redesigning a Performance Monitoring Framework for Trilinos Developers using DASH. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 337–345.
• K. Harding and D. M. Dunlavy, Improving Runtime Performance of Tensor Computations using Rust from Python. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 346–356.
• A. Knigge, S. Swan, and J. Green, Overhead Quantification of the Lightweight Distributed Metric Service for High-Performance Computers. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 357–362.
• J. Rhyne and I. Yamazaki, An Investigation of High Precision Computation using Lower Precision Kernels. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 363–378.
• A. Shumer, Y. Ho, and C. Siefert, Evaluating the Cerebras Wafer Scale Engine 3 Against the NVIDIA H100 GPU for LLM Training and Inference. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 379–387.
• J. Wesley, M. G. F. Dosanjh, and W. Schonbein, Assessing the Impact of MPI Process Placement on Communication in Many-Core Systems. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 388–403.
• M. Taylor, C. Pearson, L. Berger-Vergiat, and G. Long, Implementation of Conjugate Gradient Solver on Tenstorrent’s Wormhole. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 404–417.
• A. Fernandez and J. Lofstead, Integrating Machine Learning and Finite Element Analysis. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 419–426.
• E. Jermann, N. Ellingwood, and C. M. Siefert, Evaluation of LLM-Generated Kokkos Code using Compile-Time and Run-Time Testing. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 427–436.
• R. Kasamsetty, N. Winovich, and C. M. Siefert, Processing of Scientific Data for Large Language Models. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 437–470.
• A. M. Propp, M. Perego, E. C. Cyr, A. Gruber, A. Howard, A. Heinlein,P. Stinis and D. Tartakovsky, Scalable Ice Sheet Surrogate Modeling Via Domain-Decomposed, Physics-Inspired Graph Neural Networks. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 471–488.
• D. Rodriguez and M. Perego, Coupled Deep Neural Operators for Accelerating Uncertainty Quantification of Ice-Sheet Dynamics. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 489–508.
• C. Sahasrabudhe, Y. Ho, N. Winovich, and S. Rajamanickam, Data Extraction and Federated Learning for LLM Training. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 509–516.
• E. C. Weber, P. A. Bosler, and J. A. Actor, Data-Integrated Earth System Modeling via Neural Ordinary Differential Equations: Temperature Reconstruction. Technical Report SAND2025-14267O, Sandia National Laboratories, 2025, pp. 517–528.