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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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NASA NTRS2026

Benchmarking Bayesian Optimization Frameworks and Acquisition Strategies for Materials Discovery and Autonomous Laboratories

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Bayesian optimization↗

NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗

NASA NTRS1977

Inventory of forest resources (including water) by multi-level sampling

The author has identified the following significant results. A stratified random sample using LANDSAT band 5 and 7 panchromatic prints resulted in estimates of water in counties with sampling errors less than + or - 9% (67% probability level). A forest inventory using a four band LANDSAT color composite resulted in estimates of forest area by counties that were within + or - 6.7% and + or - 3.7% respectively (67% probability level). Estimates of forest area for counties by computer assisted techniques were within + or - 21% of operational forest survey figures and for all counties the difference was only one percent. Correlations of airborne terrain reflectance measurements with LANDSAT radiance verified a linear atmospheric model with an additive (path radiance) term and multiplicative (transmittance) term. Coefficients of determination for 28 of the 32 modeling attempts, not adverseley affected by rain shower occurring between the times of LANDSAT passage and aircraft overflights, exceeded 0.83.

Aldrich, R. C.↗

NASA NTRS1979

Machine processing of Landsat MSS data and DMA topographic data for forest cover type mapping

A study with the objective of developing and testing techniques which utilize both digital topographic data and Landsat MSS spectral data to map forest cover types is examined. Emphasis is given to the topographic distribution model (TDM), which combines point-by-point information about forest species, elevation, slope, and aspect to quantitatively describe topographic positions. Results show the stratified random sample approach to be very effective for developing the TDM, while the use of topographic data significantly improved the overall classification accuracy of forest cover types as compared to using spectral data alone.

Fleming, M. D.↗

NASA NTRS1970

The use of space and high altitude aerial photography to classify forest land and to detect forest disturbances

In October 1969, an investigation was begun near Atlanta, Georgia, to explore the possibilities of developing predictors for forest land and stand condition classifications using space photography. It has been found that forest area can be predicted with reasonable accuracy on space photographs using ocular techniques. Infrared color film is the best single multiband sensor for this purpose. Using the Apollo 9 infrared color photographs taken in March 1969 photointerpreters were able to predict forest area for small units consistently within 5 to 10 percent of ground truth. Approximately 5,000 density data points were recorded for 14 scan lines selected at random from five study blocks. The mean densities and standard deviations were computed for 13 separate land use classes. The results indicate that forest area cannot be separated from other land uses with a high degree of accuracy using optical film density alone. If, however, densities derived by introducing red, green, and blue cutoff filters in the optical system of the microdensitometer are combined with their differences and their ratios in regression analysis techniques, there is a good possibility of discriminating forest from all other classes.

Aldrich, R. C.↗

NASA NTRS1982

Detection of variations in aspen forest habitat from LANDSAT digital data: Bear River Range, Utah

The aspen forests of the Bear River Range were analyzed and mapped using data recorded on July 2, 1979 by the LANDSAT III satellite; study efforts yielded sixty-seven light signatures for the study area, of which three groups were identified as aspen and mapped at a scale of 1:24,000. Analysis and verification of the three groups were accomplished by random location of twenty-six field study plots within the LANDSAT-defined aspen areas. All study plots are included within the Cache portion of the Wasatch-Cache National Forest. The following selected site characteristics were recorded for each study plot: a list of understory species present; average percent cover density for understory species; aspen canopy cover estimates and stem measurements; and general site topographic characteristics. The study plot data were then analyzed with respect to corresponding Landsat spectral signatures. Field studies show that all twenty-six study plots are associated with one of the three aspen groups. Further study efforts concentration on characterizing the differences between the site characteristics of plots falling into each of the three aspen groups.

Merola, J. A.↗

NASA NTRS1975

Land use and pollution patterns on the Great Lakes

The author has identified the following significant results. The final mapping of the large watersheds of the Manitowoc and the Oconto was done using the 25% sampling approach. Comparisons were made with earlier strip mapping efforts of the Oconto and Manitowoc watersheds. Regional differences were noted. Strip mapping of the Oconto resulted in overestimation of the amount of agricultural land compared to the random sampling method. For the Manitowoc, the strip mapping approach produced a slight underestimate of agricultural land, and an overestimate of the forest category.

Haugen, R. K.↗

NASA NTRS1976

The usefulness of Skylab/EREP S-190 and S-192 imagery in multistage forest surveys

The author has identified the following significant results. The RMSE of point location achieved with the annotation system on S190A imagery was 100 m and 90 m in the x and y direction, respectively. Potential gains in sampling precision attributable to space derived imagery ranged from 4.9 to 43.3 percent depending on the image type, interpretation method, time of year, and sampling method applied. Seasonal variation was significant. S190A products obtained in September yielded higher gains than those obtained in June. Using 100 primary sample units as a base under simple random sampling, the revenue made available for incorporating space acquired data into the sample design to estimate timber volume was as high as $39,400.00.

Langley, P. G.↗

NASA NTRS1981

FOCIS: A forest classification and inventory system using LANDSAT and digital terrain data

Accurate, cost-effective stratification of forest vegetation and timber inventory is the primary goal of a Forest Classification and Inventory System (FOCIS). Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine processing techniques to extract and process tonal, textural, and terrain information from registered LANDSAT multispectral and digital terrain data. Comparison of samples from timber strata identified by conventional procedures showed that both have about the same potential to reduce the variance of timber volume estimates over simple random sampling.

Strahler, A. H.↗