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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Current Progress on the High-Field Cable Testing Facility at Fermilab

Fermi National Accelerator Laboratory (Fermilab) is constructing a new High Field Vertical Magnet Test Facility (HFVMTF) with capabilities comparable to the European facilities EDIPO and FRESCA2. The facility, located at Fermilab, will feature a background magnetic field of 15 T, generated by a magnet provided by Lawrence Berkeley National Laboratory (LBNL). The HFVMTF will support two U.S. national programs under the DOE Office of Science: the U.S. Fusion Energy Science (FES) program and the Magnet Development Program (MDP). It will enable the testing of HTS samples in high magnetic fields across a wide range of temperatures and facilitate the development of hybrid magnets combining LTS and HTS superconductors. This paper presents an overview of the current progress in constructing the facility, including updates on the test cryostat, power supplies, and systems for quench protection and monitoring.

Velev, G. [Fermilab]↗
NASA NTRS2005

The Need for Fusion Propulsion

Fusion propulsion is inevitable if the human race remains dedicated to exploration of the solar system. There are fundamental reasons why fusion surpasses more traditional approaches to routine crewed missions to Mars, crewed missions to the outer planets, and deep space high speed robotic missions, assuming that reduced trip times, increased payloads, and higher available power are desired. A recent series of informal discussions were held among members from government, academia, and industry concerning fusion propulsion. We compiled a sufficient set of arguments for utilizing fusion in space. If the U.S. is to lead the effort and produce a working system in a reasonable amount of time, NASA must take the initiative, relying on, but not waiting for, DOE guidance. In this talk those arguments for fusion propulsion are presented, along with fusion enabled mission examples, fusion technology trade space, and a proposed outline for future efforts.

Cassibry, Jason↗
DOE OSTI2024

Development of multi-scale computational frameworks to solve fusion materials science challenges

Over the past two decades, the US-DOE has funded multiple projects that rely on high-performance computing and exascale computing platforms to accelerate scientific discoveries and address grand scientific challenges, such as harnessing fusion energy. In this article, we review in detail one of these efforts aimed at enhancing our capability to model plasma-facing materials subject to plasma and high-energy ion/neutron irradiation. The plasma surface interactions project has built a multi-scale modeling framework where many of the plasma- and high-energy ion/neutron irradiation-induced effects occurring in tungsten are explored. Here, this knowledge is used to develop atomistically-informed, high-fidelity continuum and meso-scale models that can be validated against experiments. We review the developments within this project, with attention to experimental validation efforts, and specifically highlight activities associated with: helium bubble bursting and equation of state, and hydrogen-helium interactions in tungsten; atomistically-informed model development for beryllium-tungsten material mixing; coupling of scrape-of-layer plasma, sheath and material models; and coupling of stochastic cluster-dynamics and crystal plasticity models to address radiation effects in tungsten under stress. Finally, we present how the project is preparing for future computational architectures, for instance through efforts to adapt atomistic methods to exascale computing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Materials Assessment for FLiBe Fusion Blankets

This evaluation was prepared at the request of DOE-FES and expands on an earlier evaluation prepared by Wilson, Wiffen and Keiser in 2015 (attached as Appendices A and B). Additional useful information for materials in fission molten salt reactor systems that use FLiBe can be found in a prior NRC report [Busby 2019]. The purpose of this report is to review the considerations that will determine the fusion blanket structural materials that will be suitable for use in a FLiBe breeder-coolant fusion power plant (FPP). Identifying a fusion-relevant structural material that is sufficiently compatible with FLiBe is a key issue to be addressed. The constraints and trade-offs made in structural materials selection include the usual power generation requirements such as mechanical properties, fabricability and durability, which assumes reasonable compatibility with the working fluid. For an FPP, additional constraints include neutron economy (breeding ratio), resistance to irradiation damage and low activation (i.e. waste disposal) characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluation of Getters for Helium Purification

• Helium-3 is a vital commodity in both medical and defense applications • Ultra-low cryogenic coolant (MRI imaging, etc.) • Neutron detectors • Being investigated as possible fusion fuel • Managed and distributed by DOE-OS • New PDRD project exploring helium isotope separation • Limited supply • Only available from tritium decay • Prohibitively difficult to separate from helium-4 • SRS Tritium Facility provides a steady source

Angelette, Lucas M.↗
DOE OSTI2024

Cryogenic and safety design of the future high field cable test facility at Fermilab

The HFVMTF (High Field Vertical Magnet Test Facility) is a new experimental facility under development at Fermi National Accelerator Laboratory (FNAL) to test large superconducting magnets (up to 20 tons weight and 1.3 m diameter) in a double bath superfluid helium cryostat (1.9 K and 1.2 bar). Coupled with a superconducting dipole magnet fabricated by Lawrence Berkeley National Laboratory (LBNL), this facility will be able to test future high-temperature superconductor (HTS) cables under a background magnetic field of 15 T for fusion magnets. This paper describes the design of the cryostat and its 1.4-meter diameter lambda plate, as well as the different components for a safe operation of the facility, even during critical events such a magnet quench or a vacuum breaking situation. The project is funded by US DOE Offices of Science, High Energy Physics (HEP), and Fusion Energy Sciences (FES).

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Supporting Fusion at the Idaho National Laboratory

The Fusion Safety Program at Idaho National Laboratory has supported efforts to develop magnetic and inertial confinement fusion energy devices for commercial energy power development since 1979. In addition to our DOE less than hazard category 3 tritium handling facility STAR, we also have unique capabilities such as an adaptation of the severe accident code MELCOR developed specifically for fusion, and integrated modeling capabilities using the MOOSE Finite Element Code. In this presentation, we cover our unique experimental capabilities for investigating tritium sorption, solubility and permeation including in molten salts. We also show the application of MELCOR-TMAP to predict thermal hydraulics and tritium migration in normal and accident conditions. Finally, we demonstrate high-fidelity modeling capabilities found within the open-source MOOSE finite element framework as well as fuel-cycle modeling using TMAP8.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Fusion Energy Sciences Network Requirements Review: Mild-cycle Update

The US Department of Energy (DOE) Office of Science (SC) world-class research infrastructure provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance core DOE mission science for its sponsor SC program and to stimulate a rich discovery and innovation ecosystem. Research communities gather and flourish around each user facility, bringing together diverse perspectives. The continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

A Data Processing Pipeline To Extract A Knowledge Graph From Sec Documents For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest (disk) expressed as a latitude/longitude point and distance, and a set of SEC form types from which to extract entities and relations. There are three main components to this pipeline as currently implemented: Social Network Extraction, Critical Infrastructure Network Extraction, and Inference and Fusion. First, Social Network Extraction, implemented as the `organizations_sec` component of the workflow graph queries the SEC EDGAR webservice using the list of initial companies from the configuration file. Given this, it extracts metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Second, the Critical Network Extraction component extracts entities and relations for a critical infrastructure sector. Currently, we focus on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Third, the Inference and Fusion component relates the social network graph to the critical infrastructure graph in order to understand the impact of a company within a geographic region. Relations include ownership of the EV Charging Station asset as well as maintenance/ownership of the EV payment networks. The fused network can be represented in many ways and currently we emit a knowledge graph.

Weaver, GabrielA.↗
DOE OSTI2024

BeyondFingerprinting: AI-guided discovery of robust materials & processes

BeyondFingerprinting was a 2021-2024 Sandia Grand Challenge LDRD exploring the potential to develop new resilient materials and manufacturing processes by taking an artificial-intelligence (AI)-guided approach that integrates human-subject-matter expertise with algorithms enriched with physics-based constraints to unearth process-structure-property correlations. Such algorithms, trained on high-throughput experiments and simulations, are shown to serve as surrogate models that efficiently detect key “fingerprints” in materials data, prognose material performance, and guide effective process improvements. To accelerate broader adoption across mission areas, this AI-guided approach was demonstrated with three complex process-centric exemplars: electroplating, physical vapor deposition, and laser powder bed fusion. Together, these exemplars impact nearly every hardware component relevant to DOE and NNSA national security missions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2026

The Trajectory of Recent Solid State Fusion Results

Both NASA and Google have explored and funded Low Energy Nuclear Reaction (LENR) aka Solid-State Fusion or Lattice Confinement Fusion (LCF) research. NASA has funded efforts since 1989, and Google Research began in 2014. Google, and researchers initially-funded by Google, published significant scientific papers in Nature, Nature Communications and the Journal of Applied Physics. NASA began a significant set of LENR-triggering programs in 2012 resulting in papers in Physical Review C, the Journal of Electroanalytical Chemistry and the Journal of Condensed Matter Nuclear Science. Both NASA and Google engaged researchers across fields of nuclear physics, chemistry, electrochemistry, material science and more. NASA built upon early novel gas pumping experiments then followed the patented work of the US Navy SPAWAR (US8,419,919, “System and Method to Generate Particles”) and experiments with the Naval Surface Warfare Centers. Google supported researchers at Lawrence Berkeley National Laboratory (LBNL), the University of British Columbia (UBC), MIT and others. This resulted in patent applications and two granted patents (US10264661B2, “Target structure for enhanced electron screening” and US10566094B2 “Enhanced electron screening through plasmon oscillations”). These separate efforts, unknown to the researchers at the time, provided the impetus for the DoE ARPA-E LENR program followed by the DARPA DSO “Mechanisms for Amplification of Fusion Reaction Rates in Solids” (MARRS) program. This document briefly describes the overlapping NASA and Google Research efforts in plasma loading and electron screening emphasizing the results of the latest paper in Nature Communications. The papers and patents cited are listed.

electron screening↗
DOE OSTI2026

Deliberate Motion Analytics Applied to CUAS Sensor Fusion

The Advanced Reactor Safeguards and Security (ARSS) program in the Department of Energy’s Office of Nuclear Energy (DOE-NE) seeks to identify new technology solutions for safeguards and security challenges associated with domestic deployment of advanced nuclear reactors. Research in the ARSS program is investigating alternative physical protection system (PPS) approaches that leverage new detection technologies. This report shows test results from a new form of artificial intelligence (AI) that is called deliberate motion analytics (DMA) when used to spatially and temporally fuse active radar and passive radio frequency (RF) detection that significantly improves detection of uncrewed aircraft systems (UASs). DMA is designed to filter out false positive alarms yet provide highly reliable intrusion detection at nuclear power plants (NPPs) and advanced small modular reactor (ASMR) perimeters. This form of AI is considered to be an enabling technology for security of the future and supports the ARSS investigation of alternative PPSs.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Estimation Methodology to Evaluate Hypothetical Downwind Impacts from Fusion Plants

The continuing move toward establishing fusion systems for power generation and the associated research to that end is prompting examination of the potential health and safety impacts of such plants to the environment and human health. As many fusion facilities will have tritium inventories on site as part of the fusion fuel, evaluating the potential for downwind impacts from fusion facilities or power plants resulting from accident or routine emissions is a general requirement for assessing location and impacts to workers and the public. As part of siting considerations and permitting, the fusion facilities would be evaluated for potential for downwind concentration and dose impacts. For accident assessment scenarios, the downwind impacts are usually modeled as an instantaneous (or near-instantaneous) release of material transported following the wind. A range of meteorological conditions are usually assessed to determine a bounding case which results in a dose exceeding a specified threshold (e.g., 95 th or 99 th percentile; DOE 2015). This report provides initial estimates of the downwind dose impacts from a potential tritium release at a fusion power plant-relevant facility and identifies potential distances required to limit impacts to nearby population. This effort is meant to provide a bounding analysis and theoretical understanding of impacts of tritium releases for facilities subject to various environmental and atmospheric conditions. Using a Gaussian dispersion model to simulate a brief plume, downwind concentration and dose is projected for tritium oxide. Releases are assumed to consist entirely of tritium oxide due to the increased dose impacts from the oxide form relative to the elemental form of tritium. We also briefly identify how climatological conditions could potentially be used to support risk profile determination.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Project Title: Demonstration High Temperature Superconducting NonPlanar Stellarator Magnet with Advanced Manufactured Assemblies

This is the final report for the project “Demonstration High Temperature Superconducting Non- Planar Stellarator Magnet with Advanced Manufactured Assemblies”, funded by DOE, and performed by Type One Energy from September, 2020, to March 2024 involving the Fusion Technology Institute at the University of Wisconsin–Madison, the Plasma Science and Fusion Center (PSFC) at the Massachusetts Institute of Technology (MIT) and Commonwealth Fusion Systems (CFS) to design and fabricate the first non-planar HTS (REBCO) coil for a high-field stellarator based on the SPARC tokamak’s VIPER cable concept. Stellarators at high fields make high-temperature superconducting magnets necessary for a compact fusion device. But the asymmetric and non-planar nature of its components, especially the magnets make it difficult for scalable producibility. To address these challenges, two promising technologies have emerged: advanced manufacturing (AM) for the supporting plates for forming the magnets, and high-temperature superconducting (HTS) cables inside the plates. AM has advanced enough to produce stellarator components with the necessary geometric complexity, size, and the precision, leading to potentially significant reductions in production time, cost, and waste. The cost of HTS tape has decreased dramatically, and progress in HTS planar magnet development has reached a point where it can be proposed for application to complex 3D non-planar magnets. The main objective of this project is to develop, demonstrate and pre-commercialize a novel, non-planar HTS coil shape that remains superconducting to achieve production scalable reductions in time and cost and performance. The proposed technology is based on the novel concept of a precision sub-scale HTS nonplanar coil assembly. This project focuses on the design, fabrication, material optimization of cable design, and validation and demonstration of the high current carrying capability of superconducting magnets and their support in a complex 3D shape needed for application to stellarator magnetic plasma confinement. The specific objectives of this research program include: (1) The successful application of metal AM to build a precision sub-scale HTS nonplanar coil, (2) An HTS cable and cross-section design that can conform to the required nonplanar coil shape (bend radii as tight as 10-cm) and remains superconducting at an engineering current density of 1.35 kA/cm 2 at 77 K and 1 tesla at the conductor (5 kA in the cable). To achieve the above challenging goals, we have formed a multidisciplinary research team consisting of members from Type One Energy and UW-Madison, MIT PSFC and CFS with complementary skills and strong facilities. The team worked collaboratively on fundamental and applied research on the following three major technical areas: (1) Design, fabrication, and optimization of non-planar HTS Cable The ultimate goal of the project is to determine if commercial REBCO tapes and additive manufacturing can be used to fabricate high field (≥ 10T) non-planar coils with tight bending radii (≃ 100mm) and with a degradation of the critical current (Ic) smaller than 20% with respect to the expected performance. We started with shorter length cable to evaluate the scalability of the production process and eventually reached multiple turns for higher magnetic fields. Our findings suggest that a stellarator coil system of a relevant size, characterized by its asymmetric and non-planar components, can be fabricated using a formed cable in plate method. This system can be simulated using a large-scale modeling approach. The use of hybrid modeling 3 techniques will be pivotal in reducing the complexity of the model and in assessing expected performance in designs. (2) Modeling and simulation of the non-planar HTS Cable Multiphysics simulations are performed using the commercial software and are carried out in self-field conditions, involving 2D and 3D models and twisted around one slot of twist-pitched VIPER cable. Multiphysics simulations are mainly focused on the coil for the critical current evaluation, the magnetic field map, self-Lorentz forces and mechanical, and magnetothermal behavior and the quench dynamics. The detailed model and prediction of the superconducting performance of a stellarator-relevant demonstration cable from numerical simulations supports the results from the actual testing backing the results. A detailed description and results are provided in the later sections. (3) Design, fabrication, and optimization of support for the non-planar HTS Cable The team developed an additive manufactured (AM) coil positioning plate that formed into the required non-planar geometry (with bend radii as tight as 10-cm) and to acceptable tolerances required for a stellarator magnet: (+0.25-mm from ideal on dimensions of coil positioning plates and up to +1-mm from ideal for position of wound coil). The plate materials is also included in this selection process from fabrication and 3D printing perspective and commensurate with eventual application to a fusion reactor. From the cost effectiveness point of view, the HTS coil and plate has the potential to cost less than that made in conventional methods with less waste (<75% waste) reducing time (<50%) and cost (<50%), especially as the AM field matures. The application of advanced manufacturing in the construction of the support plates will also lead to cost reduction, as the cables can be easily replaced, thereby making the assembly modular. With such high primary cost and time savings, high current densities and magnetic field, the funded R&D work has validated the designs, proven the feasibility, and characterized the performance of the HTS coil and plate assembly, paving the way for a relevant-size stellarator coil system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

THERMAL STRUCTURAL AND SHOCK EVENT EVALUATIONS OF THE FUELING PELLET INJECTION SYSTEM FOR ITER

The US is among seven partner nations in a collaborative effort to design, build, and demonstrate fusion’s ability to be a large-scale carbon free energy source. Each country has its own Domestic Agencies (DA) that contribute directly to the ITER project. US ITER, which is a DOE Office of Science project managed by Oak Ridge National Laboratory, is developing world class engineering solutions to the design, construction, and assembly of the burning plasma experiment that can demonstrate the scientific and technological feasibility of fusion. US ITER’s scope includes completing the preliminary and final design, qualifying materials and processes for manufacture and testing, executing manufacturing, and delivering the Fueling Pellet Injection System (FPIS) to the ITER site for assembly. The US-ITER FPIS system is designed to inject cryogenically frozen pellets of deuterium-tritium (D-T) into the plasma. The FPIS has two main functions: 1. Provide a steady supply of deuterium and tritium fuel, 2. Mitigating the impact of edge localized modes on the plasma facing components. The FPIS will have the capability to reside in three port cells within a tritium second barrier containment cask. Each pellet cask contains three flight tubes linking with penetrations on the torus cryopump housing and vacuum vessel (VV): two for the magnetic high field side (HFS) pellet injection and one for the magnetic low field side (LFS) pellet injection. This paper presents results of the thermal-structural analyses of the FPIS flight tube structural components when subjected to various loads such as electromagnetic (EM) loads, nuclear heating, seismic, and operational, and dynamic shock events. The resulting temperatures and stresses under combined conditions have been found to satisfy the design criteria to ensure safe and reliable operation of the FPIS flight tubes within the vacuum vessel.

Martinez, Oscar↗
DOE OSTI2024

2019 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant

The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is necessary to meet the continual challenging national workforce needs that arise as computational science and engineering problems continue to grow in scope and complexity. Computational science and engineering (CSE) is a multidisciplinary approach that uses scientific computing to solve practical problems methods and to supply technical tools across the scientific discovery spectrum. In particular, the DOE CSGF emphasizes high-performance computing (HPC) that enables CSE that advances science and engineering in directions important to the DOE and the economy in general. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines, such as biology and cosmology, have been transformed through the augmentation of scientific observation via HPC. At government laboratories and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, renewable energy, fusion-reactor design, additive manufacturing, nanomaterials for next-generation batteries and transistors, and turbine and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development — including continuing to rise to the challenge of pandemic-related research. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing.” An explosion in scientific and technological data has driven the need for increasingly sophisticated HPC to transform those data into scientific understanding. With access to more and more data and the proliferation of HPC, Machine Learning and Artificial Intelligence are experiencing a renaissance, complementing the now well-established use of computational simulation. Indeed, in its September 2020 subcommittee report on “AI/ML, Data Intensive Science and High-Performance Computing”, the DOE Advanced Scientific Computing Advisory Committee (ASCAC) explicitly called for a fellowship program to train computational and data scientists to tackle exascale and data-intensive computing challenges. This collaboration of empirical and theory-based modeling will increasingly inform federal policymakers whose decisions affect American society and future generations, and it requires highly skilled and intellectually agile computational scientists who can support the fast-moving DOE National Laboratory research environment. In fact, the DOE CSGF program has explicitly and consistently addressed this need.

97 MATHEMATICS AND COMPUTING↗