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NASA NTRS2018

NASA's Small Spacecraft Systems Virtual Institute and Small Spacecraft Enterprise

In January of 2017, NASA's Space Technology and Science Mission Directorates established the Small Spacecraft Systems Virtual Institute (S3VI). The mission of the agency-wide institute is to advance the field of small spacecraft systems to expand the capabilities and utility of small spacecraft to perform high-value science by promoting innovation, exploring new concepts, identifying emerging technology opportunities, and establishing effective conduits for the collaboration and the dissemination of research results relevant to small spacecraft systems and subsystems. To achieve this, the S3VI serves as the common portal for NASA-related small spacecraft activities, hosts the Small Spacecraft Body of Knowledge as an online resource for the annual Small Spacecraft Technology State of the Art report, including a components and subsystems database, and also collects and organizes related knowledge such as small spacecraft reliability processes and best practices. The S3VI also serves as the front door for other governmental, non-governmental, and external agencies that wish to collaborate or interact with NASA small spacecraft organizations. NASA also presently has a growing number of small spacecraft related programs, projects, and efforts underway to advance the utility of small spacecraft instruments, technologies, and missions to support NASA to achieve its exploration and science goals. These various activities will be outlined and described to include small spacecraft applications and supporting technologies for cis-lunar and deep space missions.

Small spacecraft↗

DOE OSTI2022

International Radiological/Nuclear Training for Emergency Response - Major Public Events Virtual Workshop: Nuclear Security Planning and Operations (Day 3) [Slides]

This module will provide an overview of best practices for nuclear security planning requirements to include building a nuclear security plan and a concept of operations. It will introduce best practices for nuclear security planning to include the following: (1) Develop a Nuclear Security Plan, (2) Review 10 key elements of a Concept of Operations, (3) Estimate resources for an MPE, and (4) Capture lessons learned and best practices.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2020

Nanometer-Scale Correlations in Aqueous Salt Solutions

The intermediate-range (1–4 nm) correlation of cations, an- ions, and water in aqueous alkaline earth salt solutions is measured using synchrotron X-ray diffraction. We differentiate from the entire solution structure factor, S(Q), a separate region at low Q (<1 °A-1) containing local diffraction maxima (pre-peaks) that indicate nanometer-scale oscillatory behavior. These features are quantitatively reproduced with classical molecular dynamics simulations. At high concentrations, the pre-peaks emerge from correlations arising from existence of small quasi close-packed lattice- like structures comprised of cation hydration spheres. We also analyze the concentration dependence of the pre-peak and discuss the overall results in light of the rich literature dealing with intermediate-range correlations underlying universal phenomena in concentrated electrolytes. The authors were supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences. Pacific Northwest National Laboratory (PNNL) is operated by Battelle for the U.S. Department of Energy under Contract No. DE-AC05-76RL01830. Computing resources were generously allocated by PNNL's Institutional Computing program. DOE Contract No. DEAC02- 06CH11357 supports operation of the Advanced Photon Source at Argonne National Laboratory.

Chemical physics, nucleation, Madelung potential↗

DOE OSTI2022

The locational value of distributed energy resources: A parcel-level evaluation of solar and wind potential in New York state

We report distributed energy resources (DERs) are poised to play a significant role in evolving power systems because of their flexibility to be sited in areas of high value to the grid. Traditional DER compensation frameworks, specifically net energy metering (NEM), inadequately communicate differences in the locational and temporal value of DER generation to the grid. As a transition from NEM, New York State's (NYS's) Value of Distributed Energy Resources (VDER) framework provides a methodology for calculating different value components that DERs offer the grid. To study the impacts of VDER frameworks on DER deployment, we developed a model to assess the value of distributed solar and wind systems configured as either a behind-the-meter system or a front-of-the-meter system for each parcel of land in NYS. Using parcel-level granularity, we can closely evaluate DER locational value and siting availability, particularly in urban and suburban settings. Our analysis finds while most DER generators would be modestly compensated for deferred transmission and distribution infrastructure upgrades (~0.01 $\$$/kWh for solar PV), a subset of projects receive significant value from this component (0.035 to 0.089 $\$$/kWh for top 5% of parcels for solar PV). Finally, our analysis finds VDER provides less overall compensation than NEM to the average DER generator, however, it increases access to compensation for front-of-the-meter DERs - a large and emerging market.

14 SOLAR ENERGY↗

DOE OSTI2023

Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Toward ML-Assisted Advanced Manufacturing

Abstract The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery from an emerging advanced manufacturing process, shear-assisted processing and extrusion (ShAPE). We generate realistic images conditioned on temper and either experimental parameters or material properties. In doing so, we are able to integrate machine learning into the development cycle, by allowing a user to immediately visualize the microstructure that would arise from particular process parameters or properties. This work forms a technical backbone for a fundamentally new approach for understanding manufacturing processes in the absence of first-principle models. By characterizing microstructure from a topological perspective, we are able to evaluate our models’ ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples. Our method is successful in capturing the visual and general microstructural features arising from the considered process, with analysis highlighting directions to further improve the topological realism of our synthetic imagery.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Rapid Adaptation of Chemical Named Entity Recognition Using Few-Shot Learning and LLM Distillation

Named entity recognition (NER) has been widely used in chemical text mining for the automatic identification and extraction of chemical entities. However, existing chemical NER systems primarily focus on scenarios with abundant training data, requiring significant human effort on annotations. This poses challenges for applications in the chemical field, such as catalysis, where many advancements have traditionally relied on trial-and-error investigations and incremental adjustment of variables. This hinders catalysis science and technology progress in addressing emerging energy and environmental crises. In this work, we propose a few-shot NER model that can quickly adapt to extract new types of chemical entities by using only a limited number of annotated examples. Our model employs a metric-learning approach to transfer entity similarity knowledge from high-resource chemical domains (with abundant annotations) to enable effective entity recognition in low-resource specialized domains (limited annotation). We validate the effectiveness of our model on a few-shot chemical NER benchmark built based on six existing chemical NER data sets. Experiments show that the proposed few-shot NER model can achieve reasonable performance with only 5 examples per entity type and shows consistent improvement as the number of examples increases. Furthermore, we demonstrate how the proposed model can be trained with large language model (LLM) annotated data, opening a new pathway for rapid adaptation of NER systems. Furthermore, our approach leverages the knowledge broadness of large language models for chemistry while distilling this knowledge into a lightweight model suitable for efficient and in-house use.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

2019 Project Peer Review Report

This document summarizes the evaluations provided by an independent external panel of experts at the 2019 U.S. Department of Energy Bioenergy Technologies Office's Peer Review meeting on March 4–7, 2019 in Denver, CO. BETO manages a diverse portfolio of technologies covering the full spectrum of bioenergy production, from the feedstock source to end use. BETO systematically prioritizes research and development (R&D) into technology opportunities across a range of emerging scientific breakthroughs and technology-readiness levels. This approach supports a diverse R&D portfolio while developing the most promising and widely applicable technologies, testing technologies as integrated processes, and verifying integrated processes at the engineering scale. These technologies will use a broad variety of currently underused domestic biomass and waste resources to produce increasing volumes of biofuels, bioproducts, and biopower. The biennial peer review process enables external stakeholders to provide feedback on the responsible use of taxpayer funding and develop recommendations for the most efficient and effective ways to accelerate the development of a bioenergy industry. BETO completed these reviews in 2019. This report includes the results of both the Project Peer Review meeting held in March 2019 and the Program Management Review meeting held in July 2019.

09 BIOMASS FUELS↗

DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Quantifying model prediction sensitivity to model-form uncertainty

Computational and mathematical models are essential to understanding complex systems and phenomena. However, when developing such models, limited knowledge and/or resources necessitates the use of simplifying assumptions. It is therefore crucial to quantify the impact of such simplifying assumptions on the reliability and accuracy of resulting model predictions. This work develops a first-of-its-kind approach to quantify the impact of physics modeling assumptions on predictions. Here, we leverage the emerging field of model-form uncertainty (MFU) representations, which are parameterized modifications to modeling assumptions, in combination with grouped Sobol’ indices to quantitatively measure an assumption’s importance. Specifically, we compute the grouped Sobol’ index for the MFU representation’s parameters as a single importance measure of the assumption for which the MFU representation characterizes uncertainty. To ensure this approach is robust to the subjective choice of how to parameterize a MFU representation, we establish bounds for the difference between sensitivity results for two different MFU representations based on differences in model prediction statistics. The capabilities associated with this approach are demonstrated on three exemplar problems: an upscaled subsurface contaminant transport problem, ablation modeling for hypersonic flight, and nuclear waste repository modeling. We found that our grouped approach is able to assess the impact of modeling assumptions on predictions and offers computational advantages over classical Sobol’ index computation while providing more interpretable results.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Quantum Computing Technology Roadmaps and Capability Assessment for Scientific Computing - An analysis of use cases from the NERSC workload

The National Energy Research Scientific Computing Center (NERSC), as the high-performance computing (HPC) facility for the Department of Energy’s Office of Science, recognizes the essential role of quantum computing in its future mission. In this report, we analyze the NERSC workload and identify materials science, quantum chemistry, and high-energy physics as the science domains and application areas that stand to benefit most from quantum computers. These domains jointly make up over 50% of the current NERSC production workload, which is illustrative of the impact quantum computing could have on NERSC’s mission going forward. We perform an extensive literature review and determine the quantum resources required to solve classically intractable problems within these science domains. This review also shows that the quantum resources required have consistently decreased over time due to algorithmic improvements and a deeper understanding of the problems. At the same time, public technology roadmaps from a collection of ten quantum computing companies predict a dramatic increase in capabilities over the next five to ten years. Our analysis reveals a significant overlap emerging in this time frame between the technological capabilities and the algorithmic requirements in these three scientific domains. We anticipate that the execution time of large-scale quantum workflows will become a major performance parameter and propose a simple metric, the Sustained Quantum System Performance (SQSP), to compare system-level performance and throughput for a heterogeneous workload.

97 MATHEMATICS AND COMPUTING↗

NASA NTRSDate not supplied

Derivation of Capabilities and Resources for Treating Medical Conditions in Deep Space

BACKGROUND: Medical care in spaceflight requires the adaptation of terrestrial standards to the constraints of the space environment. However, there is often conflict between the ideal resources required for treatment and diagnosis of a medical condition, and the constraints on their mass, volume, data needs, etc. This optimization of trades between medical risk and resources will be a significant challenge for deep space medical system design. METHODS: A team of physicians with a broad range of expertise reviewed the 120 medical conditions on the condition list for NASA’s Informing Mission Planning via analysis of Complex Tradespaces Medical Database (IMPACT-MD). Each team member was assigned a subset of conditions based on their experience and training. The assigned physician generated a proposed list of capabilities, definitions, and resources required to treat each condition based on terrestrial practice guidelines, medical literature, and subject matter expertise. Additional specialists were consulted for conditions where expertise was not present within the main group. The proposed list was then reviewed by the broader team and modified as needed to achieve consensus. Each capability and resource was then assigned parameters to define quantity required per medical event, necessity, training level required, and primacy order of any alternative resources. These capabilities and resources were placed into condition specific tables and delivered to a team of engineers who added mass and volume data for each specific resource and converted the table into a database for use as input to a computational model to simulate spaceflight (IMPACT) DISCUSSION: This process ensures that a minimum of three subject matter experts review and agree upon the scope of practice, medical diagnostic tools, and treatment modalities that would be necessary to address emergent and non-emergent conditions that may arise during spaceflight to inform requirements during the vehicle design phase. The method is scalable to any design reference mission and permits modification of the existing database as information, conditions or experience are added. By including specialists, generalists, and those with subject matter expertise in the spaceflight environment on the team, we ensure that the included capabilities represent a realistic and actionable foundation for planning deep space missions.

D R Levin↗

NASA NTRS2024

The Future of Arctic Fires: Leveraging Ongoing Activities, New Efforts, and International Cooperation

Fires in the Arctic region are gaining more attention and driving increased concern from the public, the scientific community, and policymakers. An increasing number of scientific projects, collaboration efforts, media coverage, and policy assessments have been and are being implemented and published regarding this emerging and important wildland fire phenomenon. In short, this increased activity comes from the recognition of increasing fire risk and activity close to, within, and impacting the Arctic. There is a need to integrate these numerous and expanding local, national, and international efforts to avoid redundant and overlapping efforts and to focus resources more efficiently. As a response, this report suggests short and long-term goals to respond to the growing fire risk and fire danger in the Arctic. All efforts call for more integrated international collaboration and greater recognition of the importance for Arctic communities to be collaborators and leaders.

Future↗

DOE OSTI2022

Cumulative Effects Analysis for Wind Energy Development: Current Practices, Challenges, and Opportunities (IEA Wind White Paper)

The increasing global deployment of wind energy has given rise to concerns about potential adverse effects on certain wildlife species and habitats. The United States and European nations use environmental impact assessments (EIAs) to evaluate the environmental effects of wind energy and inform wind energy planning, siting, and operational processes. A key component of the EIA is the cumulative effects analysis/assessment (CEA). CEAs consider the effects of a proposed development in the context of past, present, and future developments, as well as other (non-wind) activities. However, practitioners worldwide have struggled to implement cost-effective and consistent processes for CEAs. Further, there is no widely accepted scientific methodology to assess cumulative effects. As wind energy deployment continues to expand, developing a consistent and scientifically based approach to CEAs may provide a more comparable across assessments and cost-effective means of reducing risk during siting, operations, and decommissioning/repowering, while minimizing regulatory hurdles. This technical report evaluates the current state of CEA practices, covering both land-based and offshore wind energy development. It focuses on impacts from the preconstruction, construction, and operational phases of the wind farm, which are the phases where most research currently exists. Emerging research addresses impacts from the perspective of life cycle assessments (LCAs), including the impacts of manufacturing and preconstruction (May et al. 2020). The technical report also summarizes CEA processes and guidelines, analysis approaches, and current challenges. Finally, it highlights opportunities for further research and coordination, and includes a geographically organized CEA information resource bank.

17 WIND ENERGY↗

DOE OSTI2022

Satellite Enveloped with STITCHED Engineering Sensors for Detection of Approaching Objects

Today as well as tomorrows spaceborne assets impact almost all areas of national and nuclear security. Spaceborne assets can not only collect and disseminate valuable data, well beyond just the visual, but also track terrestrial-based mobile assets in real-time, and active spaceborne platforms potentially pose serious risk to vulnerable earth-based systems and infrastructures. The capability to defend national spaceborne assets from attack/interference is critical for security interests. This effort supports this mission through the cost-effective preeminent detection of approaching threats to our nation’s vital resources, in order to help secure and trust these high-value assets against the threats of tomorrow. This project develops novel fabrication techniques for conformal, low-profile and lightweight leakywave antenna (LWA) detection/imaging systems, which fuses technical embroidery (TE) and laser ablation (LA) processes with LWA design. Technical embroidery is an emerging field in additive textile manufacturing where flexible materials and functionalized fabrics are created for a wide variety of uses and purposes, while laser ablation is the process of removing material from a solid surface by irradiating it with a laser beam. Here, thin, conformal antenna designs are designed, modeled and fabricated using both TE and LA, to create lightweight, flexible and conformal object detection and imaging radars. This novel development ensures our nation’s ability to field advanced lightweight and conformal technologies to protect spaceborne assets.

42 ENGINEERING↗

DOE OSTI2026

An Overview of Arizona's Electric Grid Resilience Using TASTI-GRID

This document provides a concise overview of the analytical capabilities of the TASTI-GRID team. Utilizing additional data and resources, it synthesizes platform insights to present a distinct perspective on investing in grid resilience throughout Mississippi. This document is not intended to influence or direct state- level decisions related to investments in grid resilience and reliability. TASTI-GRID should not be used for real-time monitoring, supporting the ESF #12 emergency response functions, or predicting the restoration times for outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

TASTI-GRID: An Overview of Minnesota's County and Tribal Electric Grid Resilience

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

Designing for Advanced Aerial Mobility: Human-Autonomy Teaming and In-Time System-Wide Safety Assurance

The continued growth of aviation shall require new innovative technologies and operational concepts to meet the ever-increasing demands on air transportation. The NASA Advanced Air Mobility (AAM) project focuses on emerging aviation markets, such as Urban Air Mobility (UAM). UAM is defined as “...a safe and efficient system for air passenger and cargo transportation within an urban area. It is inclusive of small package delivery and other urban unmanned aerial system services and supports a mix of onboard/ground-piloted and increasingly autonomous operations” ([1]). The AAM project emphasizes technology development and validating system-level concepts and solutions in coordination with other NASA Aeronautics Research Mission Directorate (ARMD) projects to enable UAM metro- and micro-plex vertiport and airspace concepts of operations. The NASA AAM research portfolio includes the concepts of Remote Supervisor-in-Command (RSC) and Fleet and Airspace Manager (FAM) as possible human roles for consumer fleet providers. NASA research in RSC is focused on development of guidelines and standards for remote pilots/operators passively and actively controlling a large fleet of autonomous aircraft. For FAM, flight and ground system concepts and technologies to enable high density homogeneous operations at increased scale from vertiport(s), and coordination with other humans in the systems (e.g., UAM urban airspace manager, Air Traffic Control) are key research areas. The envisioned UAM operations are posited to require autonomous systems to enable functions ranging from fleet and resource management to vehicle control. Although automation has become increasingly sophisticated and ubiquitous in civil aviation, autonomy represents a significant evolution in automation, which has generally been limited in functional scope and capability. As autonomy takes on increasing responsibilities, humans and machines will be required to work together in new and different ways [2], rather than traditional design approaches focused on how machines (i.e., autonomy) can do the work of people. The emerging field of human-autonomy teaming (HAT) represents a comprehensive and prioritized research-driven approach to enable the success of future emerging aviation market applications through capabilities and principles that facilitate humans and machine working and thinking better together. The NASA Transformational Tools and Technologies (TTT) Autonomous System (AS) Sub-project was created to assist with the transition into higher levels of autonomy to enable new modes of air transportation, such as UAM. TTT-AS has identified HAT as a key research need to enable UAM while maintaining today’s ultra-safe aviation system safety levels. The latter challenge has been taken up by the NASA System-Wide Safety (SWS) Project, which recognizes that aviation safety, as it evolves, shall require new ways of thinking about safety to include integration of a wide-range of existing and new safety systems and practices, enhanced tools and technologies, increased access to data and data fusion, improved data analysis capabilities, enhanced in-time risk monitoring and detection, hazard prioritization and mitigation, safety assurance decision-support, and in-time integrated system analytics [3].The operational concept of UAM represents a variety of work that has been termed, “work-as-imagined” to characterize the idea that how people think that work is done and how work is actually done are often not the same [4]. To ensure design success and system safety, looking at “work-as-done” provides a comparative approach toward UAM concept and technology design through examination of corresponding analogs found today in aviation (e.g., on-demand operations) and other transportation domains (e.g., port operations). The paper shall discuss various alternative applications with specific focus on airline operation center (AOC) operations, and unmanned aerial system (UAS) command-and-control to inform scaled-versions of FAM and RSC, respectively, and with consideration of the national airspace system contextual environment. The tenets and principles of the HAT field and current NASA research efforts under the TTT-AS sub-project shall also be described. Finally, the SWS sub-project efforts to develop In-Time System-Wide Safety Assurance (ISSA) and In-Time Safety Management Systems (IASMS) are discussed in terms of how “in-time” safety assurance may be conceptualized for the on-demand mobility air taxi “work-as-imagined” operational concept [5]. As part of this effort, concepts from the emerging field of resilience engineering, are being studied. Traditional approaches to aviation safety have focused on what can go wrong and how to prevent it. Another approach to thinking about system safety should reflect not only “avoiding things that go wrong” (protective safety) but also “ensuring that things go right” (productive safety), that enables a system to exhibit the resilient performance [6] necessary for the success of the future aviation system emerging concepts of operations. The paper shall describe efforts focused on how productive safety and resilience may enable a more complete approach to system safety thinking and design of ISSA and IASMS for UAM. Future directions and research needs shall also be discussed.

resilience↗

DOE OSTI2024

Speeding up and reducing memory usage for scientific machine learning via mixed precision

Scientific machine learning (SciML) has emerged as a versatile approach to address complex computational science and engineering problems. Within this field, physics-informed neural networks (PINNs) and deep operator networks (DeepONets) stand out as the leading techniques for solving partial differential equations by incorporating both physical equations and experimental data. However, training PINNs and DeepONets require significant computational resources, including long computational times and large amounts of memory. In search of computational efficiency, training neural networks using half precision (float16) rather than the conventional single (float32) or double (float64) precision has gained substantial interest, given the inherent benefits of reduced computational time and memory consumed. However, we find that float16 cannot be applied to SciML methods, because of gradient divergence at the start of training, weight updates going to zero, and the inability to converge to a local minima. To overcome these limitations, we explore mixed precision, which is an approach that combines the float16 and float32 numerical formats to reduce memory usage and increase computational speed. Our experiments showcase that mixed precision training not only substantially decreases training times and memory demands but also maintains model accuracy. Here, we also reinforce our empirical observations with a theoretical analysis. The research has broad implications for SciML in various computational applications.

97 MATHEMATICS AND COMPUTING↗