Publications
Showing 103 results for Author: Rick K. Archibald
Mar, 2026
Conference Paper
Time Acceleration Methods for Advection on the Cubed Sphere
Climate simulation will not grow to the ultrascale without new algorithms to overcome the scalability barriers blocking existing implementations. Until recently, climate simulations concentrated on the question of whether the climate is changing. The emphasis is now shifting to impact assessments, mitigation and adaptation strategies, and regional details. Such studies wil…
Mar, 2026
Conference Paper
An Adaptive Fourier Filter for Relaxing Time Stepping Constraints for Explicit Solvers
Filtering is necessary to stabilize piecewise smooth solutions. The resulting diffusion stabilizes the method, but may fail to resolve the solution near discontinuities. Moreover, high order filtering still requires cost prohibitive time stepping. This paper introduces an adaptive filter that controls spurious modes of the solution, but is not unnecessarily diffusive. Cons…
Mar, 2026
Conference Paper
Enabling Interconnected Science Workflows through an Adapter Approach
The INTERSECT Software framework project aims to create an open federated library that connects, coordinates, and controls systems in the scientific domain. It features the Adapter, a flexible and extensible interface inspired by the Adapter design pattern in object-oriented programming. By utilizing Adapters, the INTERSECT SDK enables effective communication and coordinat…
Mar, 2026
Conference Paper
Improving Predictions Under Uncertainty of Material Plasma Device Operations
Understanding the properties of materials when exposed to various plasma temperatures and fluxes is essential to the building and operating of fusion reactors. The Material Plasma Exposure eXperiment (MPEX) is an instrument currently being developed by the Department of Energy (DOE) for this purpose. MPEX is expected to come online in stages over the next five years. Proto…
Mar, 2026
Conference Paper
A Framework for Compressing Unstructured Scientific Data via Serialization
We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite element meshes. The framework employs a greedy topology preserving reordering of original nodes which allows for seamless integration into existing data processing pipelines. This reordering process d…
Mar, 2026
Conference Paper
Tuning the Interpolation Basis in a Multigrid Decomposition for Local Error Control
In the compression of scientific data, error-controlled compressors enable to considerably decrease the size of the dataset while maintaining adequate levels of accuracy. In this paper, we note that multi-level refactoring scheme such as MGARD i) rely on an approximation of the data based on the interpolation of coefficients, ii) estimate the resulting error with global me…
Mar, 2026
Conference Paper
Uncertainty-aware inverse modeling for federated scientific discovery across DOE facilities
Mar, 2026
Conference Paper
Privacy Preserving Federated Learning for Advanced Scientific Ecosystems
We present a framework to provide privacy preserving (PP) federating learning (FL) across multiple computational and experimental facilities. This work joins the compute capabilities of National Energy Research Scientific Computing Center (NERSC) and Oak Ridge National Laboratory Research Cloud (ORC) with simulated experimental data, such as those produced at the SLAC Nati…
Aug, 2025
Journal
Assimilating partial observation to enhance feedback control of stochastic dynamical systems
In this paper, we present a novel methodology to tackle feedback optimal control problems in scenarios where the exact state of the controlled process is unknown. It integrates data assimilation techniques and optimal control solvers to manage partial observation of the state process, a common occurrence in practical scenarios. Traditional stochastic optimal control method…
Jun, 2025
Conference Paper
Privacy Preserving Federated Learning for Advanced Scientific Ecosystems
We present a framework to provide privacy preserving (PP) federating learning (FL) across multiple computational and experimental facilities. This work joins the compute capabilities of National Energy Research Scientific Computing Center (NERSC) and Oak Ridge National Laboratory Research Cloud (ORC) with simulated experimental data, such as those produced at the SLAC Nati…
May, 2025
Journal
StOKeDMD: Streaming Occupation kernel dynamic mode decomposition
Dynamic mode decomposition (DMD) has become a common technique for constructing surrogate models for dynamical systems from observed system states. The Occupation Kernel DMD (OKDMD) method proposed in (Rosenfeld et al., 2022) and (Rosenfeld et al., 2024) is a Liouville operator based method that builds surrogate models from system state trajectories. This paper proposes an…