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

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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Machine Learning Approach for Hourly Traffic Prediction Used in EV-Charging Sites

Reliable forecasting of hourly traffic volumes on highways is critical for planning and operating electric-vehicle charging infrastructure without overloading the grid. In this work, we develop and evaluate a station-specific machine-learning approach based on NeuralProphet, enhanced with conditional seasonality to better distinguish weekday, weekend, and holiday patterns. For each station, the model automatically retrieves the same calendar day from the prior years as an AR-Net initialization, fits trend and Fourier-based seasonality components, and then applies short-term auto-regressive corrections. We train and test on 2021 and 2022 TMAS data, respectively, and validate performance over the whole year. We chose to demonstrate how the model performs on a typical weekday (3/15/2022), weekend (3/27/2022), and a special holiday (12/25/2022). Our results yield MAPE of 7.4%, 23.6%, and 32.0%, respectively. Over the entire year 2022, the overall MAPE was 17%. This demonstrates that station-specific models with conditional seasonality can achieve accurate, scalable hourly forecasts for EV-charging load planning.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS2019

Prediction of Pushback Times and Ramp Taxi Times for Departures at Charlotte Airport

When optimizing the takeoff sequence and schedule for departures at busy airports, it is important to accurately predict the taxi times from gate to runway because those are used to calculate the earliest possible takeoff times. Several airports like Charlotte Douglas International Airport show relatively long taxi times inside the ramp area with large variations, with respect to the travel times in the airport movement area. Also, the pushback process times have not been accurately modeled so far mainly due to the lack of accurate data. The recent deployment of the integrated arrival, departure, and surface traffic management system at Charlotte airport by NASA enables more accurate flight data in the airport surface operations to be obtained. Taking advantage of this system, actual pushback times and ramp taxi times from historical flight data at this airport are analyzed. Based on the analysis, a simple, data-driven prediction model is introduced for estimating pushback times and ramp transit times of individual departure flights. To evaluate the performance of this prediction model, several machine learning techniques are also applied to the same dataset. The prediction results show that the data-driven prediction model is as good as the machine learning algorithms when comparing various prediction performance metrics.

airport surface operations↗
DOE OSTI2021

Learning curves for drug response prediction in cancer cell lines

Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A machine learning framework for elastic constants predictions in multi-principal element alloys

On the one hand, multi-principal element alloys (MPEAs) have created a paradigm shift in alloy design due to large compositional space, whereas on the other, they have presented enormous computational challenges for theory-based materials design, especially density functional theory (DFT), which is inherently computationally expensive even for traditional dilute alloys. In this paper, we present a machine learning framework, namely PREDICT (PRedict properties from Existing Database In Complex alloys Territory), that opens a pathway to predict elastic constants in large compositional space with little computational expense. The framework only relies on the DFT database of binary alloys and predicts Voigt–Reuss–Hill Young’s modulus, shear modulus, bulk modulus, elastic constants, and Poisson’s ratio in MPEAs. We show that the key descriptors of elastic constants are the A–B bond length and cohesive energy. The framework can predict elastic constants in hypothetical compositions as long as the constituent elements are present in the database, thereby enabling property exploration in multi-compositional systems. We illustrate predictions in a FCC Ni-Cu-Au-Pd-Pt system.

Linton, Nathan (ORCID:0000000315485613)↗
DOE OSTI2021

Knowledge-Guided Machine Learning (KGML) Platform to Predict Integrated Water Cycle and Associated extremes

Focal Area(s): Predictive modeling through the use of AI techniques and insight gleaned from complex data (both observed and simulated). Science Challenge: Although advanced predictive capabilities of the water cycle are critical to address environmental needs and develop sustainable solutions for energy demands, there is no robust framework, to say the least, that seamlessly integrates local to intermediate to global scales and a gamut of biogeophysical information to enhance understanding of the integrated water cycle and its associated extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Using Machine Learning to Develop a Predictive Understanding of the Impacts of Extreme Water Cycle Perturbations on River Water Quality

This whitepaper addresses to two focal areas – (3) Insight gleaned from complex data using Artificial Intelligence (AI), and other advanced techniques (primary), and (2) Predictive modeling through the use of AI techniques and AI-derived model components (secondary). This topic is directly relevant to four DOE Earth and Environmental Systems Science Division Grand Challenges: integrated water cycle, biogeochemistry, drivers and responses in the Earth system, and data-model integration.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Using Machine Learning to Develop a Predictive Model for Future Fire Seasons

The deep learning model shows promise for predicting areas of high wildfire potential. Full evaluation of the model performance is ongoing. Currently, the developed deep learning model is better overall at predicting the number of fires over the acres burned. Acres burned is dependent on location, suppression plan, and current conditions. Antecedent conditions are only one piece of the equation. In-season changes are not accounted for. An ignition source is required, which further complicates the model training and prediction.

White, Andrew T.↗