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NASA NTRSDate not supplied

Component Level Regression Testing in a Hierarchical Architecture

The Goddard Earth Observing System (GEOS) is an Earth system model consisting of a large suite of individual model components that can be coupled in a flexible manner to investigate a variety of Earth science issues. Specific GEOS model configurations are composed as a hierarchical collection of components based on the Earth System Modeling Framework (ESMF). Regression testing of GEOS is currently limited to (1) full system tests that are poor at isolating specific defects and (2) a suite of unit tests which have very limited coverage. As part of our approach to improve upon the current testing situation, we have prototyped the capability to perform regression tests on individual GEOS components by leveraging and extending existing checkpoint/restart capabilities. In our implementation, each ESMF component has 3 states: Import (what it needs to run), Export (which it needs to provide to other components), and Internal (the component state proper). By capturing, Import, Export and Internal states for a given component during a ull run of GEOS, a generic driver can then rerun the component offline and compare expected exports with those that have been saved. The hierarchical structure of GEOS introduces an interesting wrinkle when trying to test components that in turn drive interacting child components. To fully isolate a parent component, we use the approach of software mocks, in which the exports of children are also saved during the initial capture run of GEOS. Then when testing the parent component, the children components are replaced by a generic mock component that produces exports from the previously saved data and ensures that that all interdependencies among children components are satisfied.

Thomas Clune↗

NASA NTRSDate not supplied

Component Level Testing in a Hierarchical Architecture

Tom Clune↗

NASA NTRS1990

Users manual for the Automated Performance Test System (APTS)

The characteristics of and the user information for the Essex Automated Performance Test System (APTS) computer-based portable performance assessment battery are given. The battery was developed to provide a menu of performance test tapping the widest possible variety of human cognitive and motor functions, implemented on a portable computer system suitable for use in both laboratory and field settings for studying the effects of toxic agents and other stressors. The manual gives guidance in selecting, administering and scoring tests from the battery, and reviews the data and studies underlying the development of the battery. Its main emphasis is on the users of the battery - the scientists, researchers and technicians who wish to examine changes in human performance across time or as a function of changes in the conditions under which test data are obtained. First the how to information needed to make decisions about where and how to use the battery is given, followed by the research background supporting the battery development. Further, the development history of the battery focuses largely on the logical framework within which tests were evaluated.

Lane, N. E.↗

NASA NTRS2009

Proceedings of the First NASA Formal Methods Symposium

Topics covered include: Model Checking - My 27-Year Quest to Overcome the State Explosion Problem; Applying Formal Methods to NASA Projects: Transition from Research to Practice; TLA+: Whence, Wherefore, and Whither; Formal Methods Applications in Air Transportation; Theorem Proving in Intel Hardware Design; Building a Formal Model of a Human-Interactive System: Insights into the Integration of Formal Methods and Human Factors Engineering; Model Checking for Autonomic Systems Specified with ASSL; A Game-Theoretic Approach to Branching Time Abstract-Check-Refine Process; Software Model Checking Without Source Code; Generalized Abstract Symbolic Summaries; A Comparative Study of Randomized Constraint Solvers for Random-Symbolic Testing; Component-Oriented Behavior Extraction for Autonomic System Design; Automated Verification of Design Patterns with LePUS3; A Module Language for Typing by Contracts; From Goal-Oriented Requirements to Event-B Specifications; Introduction of Virtualization Technology to Multi-Process Model Checking; Comparing Techniques for Certified Static Analysis; Towards a Framework for Generating Tests to Satisfy Complex Code Coverage in Java Pathfinder; jFuzz: A Concolic Whitebox Fuzzer for Java; Machine-Checkable Timed CSP; Stochastic Formal Correctness of Numerical Algorithms; Deductive Verification of Cryptographic Software; Coloured Petri Net Refinement Specification and Correctness Proof with Coq; Modeling Guidelines for Code Generation in the Railway Signaling Context; Tactical Synthesis Of Efficient Global Search Algorithms; Towards Co-Engineering Communicating Autonomous Cyber-Physical Systems; and Formal Methods for Automated Diagnosis of Autosub 6000.

Denney, Ewen↗

DOE OSTI2025

Testing and Expertise for Marine Energy (TEAMER) Program Support: MRE Dynamic Seals Performance Investigation (CRADA Final Report)

This TEAMER project allows NREL to use and augment a custom special-purpose testing system to perform accelerated-life testing of rotating seals for use in the Marine Renewable Energy (“MRE”) industry. The requirements of this industry are different from most applications including marine, and relevant performance information is lacking. In order to achieve low costs with extremely effective and reliable operation over long periods deployed underwater, more study of the best seals and arrangements of seals must be performed. By developing a rigorous framework for such testing, NREL hopes to greatly improve understanding of the relevant factors and provide significant data to the industry. Further, this work can inform standards developments for this critical component of MRE systems, leading to maturation and improved acceptance of the industry in commerce.

16 TIDAL AND WAVE POWER↗

NASA NTRS2021

Lunar Asset Messaging and On Orbit Navigation

NASA has titled its 2020 thrust for the Moon, Artemis. The increased focus on the Moon as a destination for future human and robotic expeditions necessitates general purpose navigational and communications infrastructure reducing their complexity to help establish a sustained presence. A framework through which Lunar missions can relay communications and localize their positions shifts the burden from the individual mission and enables resource allocation tailored to mission-specific goals. During the summer of 2020, student interns under the Innovation to Flight (i2F) program at the National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL) in collaboration with the University of Colorado Boulder designed, built, and tested a prototype framework capable of providing surface assets with communication and positioning services. The team utilized the existing i2F CubeSat bus in addition to developing several CubeSat engineering development units (EDUs), a ground vehicle, and a ground station to simulate a scenario in which a lunar surface mission is supported by these services. A primary goal of the summer was to develop a method for localizing the ground vehicle through trilateration. Distances are inferred from the round-trip time of flight (ToF) of radio signals between an asset and several elements. Signals were sent and received using LimeSDR software defined radios on-board both the ground vehicle and the EDUs; ToF and trilateration were calculated on a Qualcomm Snapdragon development board located within the LA MOON payload data system. The ModalAI chipset on the Qualcomm was instrumental in executing visual based position estimation. Communications was facilitated through a bent-pipe approach addressing the NASA requirement to provide solutions for in communication denied locations. The ground vehicle relayed information to other surface assets in addition to its ground station through the supporting constellation. This project demonstrates the feasibility of a lunar CubeSat constellation for the support of surface assets and explores packaging and operations of the components critical to trilateration and bent-pipe communication into a standard CubeSat form factor. When implemented, this framework will open a door for new surface missions designed with lower power requirements and increased operational access.

Huang, Calvin↗

NASA NTRSDate not supplied

A Framework for Evaluating Climate Model Performance Metrics

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is often used to add value to model projections: consensus projections have been shown to consistently outperform individual models. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, certain models reproduce climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables?e.g., outgoing longwave radiation and surface temperature. Metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument and surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing weighted and unweighted model ensembles. For example, one tested metric weights the ensemble by how well models reproduce the time-series probability distribution of the cloud forcing component of reflected shortwave radiation. The weighted ensemble for this metric indicates lower simulated precipitation (up to .7 mm/day) in tropical regions than the unweighted ensemble: since CMIP5 models have been shown to overproduce precipitation, this result could indicate that the metric is effective in identifying models which simulate more realistic precipitation. Ultimately, the goal of the framework is to identify performance metrics for advising better methods for ensemble averaging models and create better climate predictions.

Noel C Baker↗

DOE OSTI2024

Progressive transfer learning for advancing machine learning-based reduced-order modeling

Abstract To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1993

Systems aspects of COBE science data compression

A general approach to compression of diverse data from large scientific projects has been developed and this paper addresses the appropriate system and scientific constraints together with the algorithm development and test strategy. This framework has been implemented for the COsmic Background Explorer spacecraft (COBE) by retrofitting the existing VAS-based data management system with high-performance compression software permitting random access to the data. Algorithms which incorporate scientific knowledge and consume relatively few system resources are preferred over ad hoc methods. COBE exceeded its planned storage by a large and growing factor and the retrieval of data significantly affects the processing, delaying the availability of data for scientific usage and software test. Embedded compression software is planned to make the project tractable by reducing the data storage volume to an acceptable level during normal processing.

Freedman, I.↗

DOE OSTI2025

Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation

We evaluate the performance of the Karhunen-Loève Deep Neural Network (KL-DNN) framework for surrogate modeling and approximate Bayesian parameter estimation in partial differential equation models. In the surrogate model, the Karhunen-Loève (KL) expansions are used for the dimensionality reduction of the number of unknown parameters and variables, and a deep neural network is employed to relate the reduced space of parameters to that of the state variables. The KL-DNN surrogate model is used to formulate a maximum-a-posteriori-like least-squares problem, which is randomized to draw samples of the posterior distribution of the parameters. We test the proposed framework for a hypothetical unconfined aquifer via comparison with the forward MODFLOW and inverse PEST++ iterative ensemble smoother (IES) solutions as well as the state-of-the-art Fourier neural operator (FNO) and deep operator networks (DeepONets) operator learning surrogate models. Our results show that the KL-DNN surrogate model outperforms FNO and DeepONet for forward predictions. For solving inverse problems, the randomized algorithm provides the same or more accurate Bayesian predictions of the parameters than IES as evidenced by the higher log-predictive probability of both the estimated parameter field and the forecast hydraulic head. The posterior mean obtained from the randomized algorithm is closer to the reference parameter field than that obtained with FNO as the maximum a posteriori estimate.

Approximate Bayesian inference↗

DOE OSTI2026

Membrane Composition Influences Expression Yield of Plant Cytochrome P450s in E. coli Lysate-Based Cell-Free Systems

Plant cytochrome P450 enzymes are central to natural product biosynthesis, but remain difficult to express in microbial hosts due to their transmembrane nature. Lysate-based, cell-free expression systems allow supplementation with artificial membranes to support the expression and translocation of transmembrane proteins. We developed a framework to systematically test liposomal membrane compositions to enhance the plant P450 expression yield. Adjustments to common phospholipid ratios or the addition of plant galactolipids had minimal impact on expression. In contrast, blended liposomes containing Egg PC, sterol-conjugated phospholipids, and PEGylated lipids produced concentration-dependent increases in expression. Expression of an Escherichia coli mechanosensitive channel and three plant P450s improved more than 2-fold, with some P450s showing up to 14-fold enhancement. Furthermore, these findings highlight membrane composition as a key determinant of the P450 expression yield in cell-free expression systems. While P450 activity was not measured, these findings provide a framework for future workflows toward achieving functional plant transmembrane enzymes for the bioproduction of natural products.

TXTL↗

NASA NTRS2013

Assessment Environment for Complex Systems Software Guide

This Software Guide (SG) describes the software developed to test the Assessment Environment for Complex Systems (AECS) by the West Virginia High Technology Consortium (WVHTC) Foundation's Mission Systems Group (MSG) for the National Aeronautics and Space Administration (NASA) Aeronautics Research Mission Directorate (ARMD). This software is referred to as the AECS Test Project throughout the remainder of this document. AECS provides a framework for developing, simulating, testing, and analyzing modern avionics systems within an Integrated Modular Avionics (IMA) architecture. The purpose of the AECS Test Project is twofold. First, it provides a means to test the AECS hardware and system developed by MSG. Second, it provides an example project upon which future AECS research may be based. This Software Guide fully describes building, installing, and executing the AECS Test Project as well as its architecture and design. The design of the AECS hardware is described in the AECS Hardware Guide. Instructions on how to configure, build and use the AECS are described in the User's Guide. Sample AECS software, developed by the WVHTC Foundation, is presented in the AECS Software Guide. The AECS Hardware Guide, AECS User's Guide, and AECS Software Guide are authored by MSG. The requirements set forth for AECS are presented in the Statement of Work for the Assessment Environment for Complex Systems authored by NASA Dryden Flight Research Center (DFRC). The intended audience for this document includes software engineers, hardware engineers, project managers, and quality assurance personnel from WVHTC Foundation (the suppliers of the software), NASA (the customer), and future researchers (users of the software). Readers are assumed to have general knowledge in the field of real-time, embedded computer software development.

tests↗

DOE OSTI2025

A harmonized protocol to assess the single-cell performance of proton exchange membrane water electrolyzers

The harmonization of testing protocols for proton exchange membrane (PEM) electrolyzers is essential for ensuring accurate and reliable performance assessments and accelerating the development of hydrogen production technologies. This protocol provides a structured approach to PEM electrolyzer setup and testing, incorporating key considerations for test station design and single-cell characterization techniques. Polarization curves and electrochemical impedance spectroscopy (EIS) are detailed, along with best practices from academic and industry research groups to enhance data accuracy and comparability. By addressing material variability and harmonizing testing methodologies, this framework enables more precise evaluations of membrane electrode assemblies and electrolyzer components. Harmonized protocols not only streamline development efforts but also foster collaboration across institutions, ultimately supporting the commercialization of hydrogen solutions through improved stack efficiency and durability.

08 HYDROGEN↗

DOE OSTI2025

Differentiating Ligand Tailoring and Cation Incorporation as Strategies for Tuning Heterobimetallic Cerium Complexes

Tuning of redox-active complexes featuring metals with high coordination numbers by incorporation of secondary redox-inactive cations has received far less attention than it deserves. Here, appending moderate steric bulk to a tripodal ligand framework has been tested for its influence on secondary-cation-driven structural and electrochemical tuning of cerium, a lanthanide that tends to adopt high coordination numbers. A quasi -C 3 -symmetric cerium(III) complex denoted [Ce] has been prepared that features pendant benzyloxy groups, and this work demonstrates that this species offers a site capable of binding single Na + or Ca 2+ ions. Electrochemical and UV-visible spectroscopic studies reveal equilibrium binding affinity of [Ce] for Na + in acetonitrile solvent, contrasting with tight binding of all cations in all other previously studied systems of this type. The modulated cation binding can be attributed to the bulky benzyloxy groups, which impact the thermodynamics of cation binding but do not impede the formation of cerium centers with coordination number 8 upon binding of either Na + or Ca2 + . Finally, the Ce(IV/III) reduction potential was found to be tunable under the equilibrium binding conditions, highlighting the potentially significant role that controlled structural changes can play in modulating the solution properties of heterobimetallic complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

E-PINNs: Epistemic Physics-Informed Neural Networks

Physics-informed neural networks (PINNs) have demonstrated promise as a framework for solving forward and inverse problems involving partial differential equations. Despite recent progress in the field, it remains challenging to quantify uncertainty in these networks. While techniques such as Bayesian PINNs (B-PINNs) provide a principled approach to capturing epistemic uncertainty through Bayesian inference, they can be computationally expensive for large-scale applications. In this work, we propose Epistemic Physics-Informed Neural Networks (E-PINNs), a framework that uses a small network, the epinet, to efficiently quantify epistemic uncertainty in PINNs. The proposed approach works as an add-on to existing, pre-trained PINNs with a small computational overhead. We demonstrate the applicability of the proposed framework in various test cases and compare the results with B-PINNs using Hamiltonian Monte Carlo (HMC) posterior estimation and dropout-equipped PINNs (Dropout-PINNs). In our experiments, E-PINNs achieve calibrated coverage with competitive sharpness at substantially lower cost. We demonstrate that when B-PINNs produce narrower bands, they under-cover in our tests. E-PINNs also show better calibration than Dropout-PINNs in these examples, indicating a favorable accuracy-efficiency trade-off.

AI for Science↗

DOE OSTI2025

Biofoundries: Principles, Tools, and Applications

This chapter aims to provide a broad overview of biofoundries and introduces the principles, concepts, and case studies. We first outline the underlying principles of the Design-Build-Test-Learn (DBTL) framework and the role of automation, digital integration, and standardization. The chapter then explores core biofoundry technologies including robotic liquid handlers, high-throughput analytical instruments, and digital infrastructure for data management and workflow scheduling. Case studies spanning DNA assembly, protein engineering, metabolic engineering, and mammalian cell culture demonstrate the practical applications of the biofoundries. Economic and societal impacts are assessed alongside current limitations. We discuss the emerging trends including artificial intelligence integration and cloud-based distributed facilities to highlight its potential for biotechnology and the bioeconomy.

Singh, Nilmani↗

DOE OSTI2025

Antiviral discovery using sparse datasets by integrating experiments, molecular simulations, and machine learning

Computational methods have demonstrated success in identifying virucidal agents, effectively contributing to the discovery of novel virucidal molecules. In this study, we developed a machine learning (ML) model, trained on a small dataset, to predict inhibitors of human enterovirus 71 (EV71), a pathological agent that causes severe disease in children and immunocompromised adults. Despite the dataset’s limitation, comprising of only 36 compounds tested, our ML framework demonstrated significant predictive capability. Notably, experimental validation revealed that five out of the eight compounds predicted by our model from the Chinese cosmetic material list exhibited virucidal activity. The inhibitor effects displayed by the main active compounds were further confirmed by molecular dynamics simulation. This underscores the potential of our AI-driven approach to bypass data constraints in identifying active molecules against viral pathogens.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Cosmological parameter estimation with a joint-likelihood analysis of the cosmic microwave background and big bang nucleosynthesis

Here, we present a joint-likelihood analysis of big bang nucleosynthesis (BBN) and cosmic microwave background (CMB) data, consistently combining likelihoods and taking into account uncertainties in nuclear reaction rates for the first time. Bayesian inference is performed on the baryon abundance and the effective number of neutrino species, 𝑁 eff , using a CMB Boltzmann solver in combination with LINX , a new flexible and efficient BBN code. We marginalize over Planck nuisance parameters and nuclear rates to find 𝑁 eff =3.0⁢8$^{+0.15}_{−0.14}$, 2.9⁢4$^{+0.16}_{−0.15}$, or 2.96$^{+0.13}_{−0.14}$, for three separate reaction networks. This framework enables robust testing of the lambda cold dark matter paradigm and its variants with CMB and BBN data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗