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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 433 records · Page 24

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications .

Abstract not provided.

Olivier, Stephen↗

DOE OSTI2022

Data science approaches for PV field analysis.

Abstract not provided.

Braid, Jennifer↗

DOE OSTI2022

Evolving to Data Science or How to Succeed at Sandia without doing Scientific Computing.

Abstract not provided.

Phillips, Cynthia↗

DOE OSTI2022

Sandia Insights ? A Data Sciences Architecture and Framework.

Abstract not provided.

Carlson, Patrick↗

DOE OSTI2022

Chapel/C++ Interoperability: Enabling Large-Scale Data Science without Costly I/O.

Abstract not provided.

Dunlavy, Daniel↗

DOE OSTI2023

Data Science for the Advancement of Magnetically Driven Inertial Fusion [Slides]

Gravitational confinement is clearly very effective but what can we do on Earth? Magnetic confinement has been studied since around 1950. Inertial confinement has been associated with lasers for over 50 years. We have made steady progress in both MCF and ICF over the last half-century.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

Final Technical Report for DE-SC0021343

Geveci, Berk [Kitware, Inc.]↗

DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026