SEARCH · Search NASA

Results for “Distributed Learning”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Federated Deep Reinforcement Learning for Decentralized VVO of BTM DERs

The future of grid control requires a hybrid approach combining centralized and decentralized methods to fully utilize the potential of smart edge devices with artificial intelligence (AI) capabilities. This paper aims to develop and evaluate a federated deep reinforcement learning (FDRL) framework for decentralized adaptive volt-var optimization (VVO) of behind-the-meter (BTM) distributed energy resources (DERs). First, this paper models a single deep reinforcement learning (DRL) agent using the Markov Decision Process (MDP) framework for decentralized adaptive VVO of BTM DERs. Two DRL algorithms, soft actor-critic (SAC) and twin-delayed deep deterministic policy gradient (TD3), are compared for their effectiveness in optimizing VVO. Results show that TD3 outperforms SAC, achieving a 71.3% improvement in mean reward. Finally, the DRL agent is deployed within the FDRL framework, using the Flower platform, to enhance learning, provide adaptive control, and ensure data privacy for BTM DERs.

Ravi, Abhijith↗

DOE OSTI2025

Contrastive learning for robust representations of neutrino data

In neutrino physics, analyses often depend on large simulated datasets, making it essential for models to generalize effectively to real-world detector data. Contrastive learning, a well-established technique in deep learning, offers a promising solution to this challenge. By applying controlled data augmentations to simulated data, contrastive learning enables the extraction of robust and transferable features. This improves the ability of models trained on simulations to adapt to real experimental data distributions. In this paper, we investigate the application of contrastive learning methods in the context of neutrino physics. Through a combination of empirical evaluations and theoretical insights, we demonstrate how contrastive learning enhances model performance and adaptability. Additionally, we compare it to other domain adaptation techniques, highlighting the unique advantages of contrastive learning for this field. Published by the American Physical Society 2025

Wilkinson, Alex (ORCID:0000000253404506)↗

DOE OSTI2022

Introduction to Special Section: Machine Learning for Image-based Geologic Interpretation

Image-based geological interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geological structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve nearhuman-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geological interpretation in petroleum exploration and production. For example, a fault picking method applied to 3-D seismic volume data using deep learning can achieve superior performance in comparison to conventional auto-picking methods. In addition, under the new normal of low oil prices, the petroleum industry seeks cost-effective strategies such as automating traditionally labor-intensive processes. Nevertheless, the potential of applying ML to geological image interpretation is still facing a few key challenges including data scarcity, data distribution, poor data and/or label quality, data leakage, learning algorithms, model architecture, training methodologies, testing and evaluation metrics, hyper-parameters optimization, model drift, production deployment, and the like.

58 GEOSCIENCES↗

DOE OSTI2025

A Dynamic Pricing Method to Manage the Impact of EV Charging on the Grid Using RL

This work addresses the challenge of managing electrical vehicle (EV) charging loads on distribution feeders with the increase in deployment of fast charging stations. To mitigate the adverse impacts on feeder health, a novel dynamic grid-informed pricing approach is proposed. This approach leverages reinforcement learning (RL) to determine hourly charging prices based on real-time grid conditions. A synthetic environment was developed to train the reinforcement learning agent. A model of an IEEE 34-bus distribution feeder with EV charging stations has been developed in OpenDSS utilizing Caldera for realistic EV charging profiles. Test cases demonstrate that the dynamic pricing strategy achieves higher energy delivery to the EV end user at a lower cost compared to constant pricing methods, while lowering voltage deviations and congestion. This approach offers more granular price adjustments, responding dynamically to feeder conditions and potentially improving grid stability and efficiency. The communication architecture to implement this dynamic pricing method is described. This research contributes to the development of smart grid-informed charging solutions that can reduce the cost of charging to the end user while also helping the grid.

EV charging, dynamic pricing, grid-informed chargi↗

DOE OSTI2025

A dynamic pricing method to manage the impact of EV charging on the grid using RL

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Anomaly detection in Hyper Suprime-Cam galaxy images with generative adversarial networks

ABSTRACT The problem of anomaly detection in astronomical surveys is becoming increasingly important as data sets grow in size. We present the results of an unsupervised anomaly detection method using a Wasserstein generative adversarial network (WGAN) on nearly one million optical galaxy images in the Hyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like galaxies that follow the distribution of the data set; anomalous images are defined based on a poor reconstruction by the generator and outlying features learned by the discriminator. We find that the discriminator is more attuned to potentially interesting anomalies compared to the generator, and compared to a simpler autoencoder-based anomaly detection approach, so we use the discriminator-selected images to construct a high-anomaly sample of ∼13 000 objects. We propose a new approach to further characterize these anomalous images: we use a convolutional autoencoder to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images and perform UMAP clustering on these. We report detected anomalies of interest including galaxy mergers, tidal features, and extreme star-forming galaxies. A follow-up spectroscopic analysis of one of these anomalies is detailed in the Appendix; we find that it is an unusual system most likely to be a metal-poor dwarf galaxy with an extremely blue, higher-metallicity H ii region. We have released a catalogue with the WGAN anomaly scores; the code and catalogue are available at https://github.com/kstoreyf/anomalies-GAN-HSC; and our interactive visualization tool for exploring the clustered data is at https://weirdgalaxi.es.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Conceptual Machine Learning Strategy for Maintenance of Molten Salt Heat Exchanger

This report addresses the requirements of Milestone M3.2 Define maintenance scenarios and benchmarks. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Final Project Briefing - Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale

Final project briefing from PingThings for FOA 1861

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Machine Learning Classification of Molten Salt Heat Exchanger Channel Plugging using Synthetic Data

This report addresses the requirements of Milestone M3.4 AI capability to identify and predict maintenance events. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating the feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults. To determine the optimal approach to detection and localization of channel plugging, we benchmark seven different ML models: Logistic Regression, K-Nearest Neighbors (KNN), Gaussian Naïve Bayes, Support Vector Machines (SVM), Decision Tree Classifier, Random Forest Tree Classifier, and Feed-Forward Neural Network. ML algorithms are benchmarked using synthetic HX plugging data generated with computational fluid dynamics COMSOL software, with added brown noise to represent experimental noise. We show that the best performance is obtained with the Decision Tree classifier.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Accurate and Timely Forecasts of Geologic Carbon Storage using Machine Learning Methods

Carbon capture and storage is one strategy to reduce greenhouse gas emissions. One approach to storing the captured CO2 is to inject it into deep saline aquifers. However, dynamics of the injected CO2 plume is uncertain and the potential for leakage back to the atmosphere must be assessed. Thus, accurate and timely forecasts of CO2 storage via real-time measurements integration becomes very crucial. This study proposes a learning-based, inverse-free prediction method that can accurately and rapidly forecast CO2 movement and distribution with uncertainty quantification based on limited simulation and observation data. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO2 storage site operators with an effective tool for real-time decision making.

Lu, Dan↗

DOE OSTI2024

Machine learning assisted bayesian inference of mix and hot-spot conditions in NIF implosions

Experiments on the National Ignition Facility (NIF) have provided clear evidence of ablator material mixing into the Hot-Spot, leading to degraded performance. However, inferring the amount of mix and Hot-Spot conditions from typical experimental observations (e.g. x-ray spectra and images) is highly challenging. Here, we have developed an analysis method that utilizes machine learning assisted Bayesian inference to find the probability distributions of the Hot-Spot and mix conditions. This approach uses a neural network, trained on an idealized 2-dimensional representation of the Hot-Spot and mix distribution, and Bayesian inference to find the statistical distributions of Hot-Spot conditions that provide a match with observations. We have tested this method with synthetic data from simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control

This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem. We then propose a novel on-policy MARL algorithm, PowerNet, in which each agent (DG) learns a control policy based on (sub-)global reward but local states and encoded communication messages from its neighbors. Motivated by the fact that a local control from one agent has limited impact on agents distant from it, we exploit a novel spatial discount factor to reduce the effect from remote agents, to expedite the training process and improve scalability. Furthermore, a differentiable, learning-based communication protocol is employed to foster the collaborations among neighboring agents. In addition, to mitigate the effects of system uncertainty and random noise introduced during on-policy learning, we utilize an action smoothing factor to stabilize the policy execution. To facilitate training and evaluation, we develop PGSim, an efficient, high-fidelity powergrid simulation platform. Here, experimental results in two microgrid setups show that the developed PowerNet outperforms the conventional model-based control method, as well as several state-of-the-art MARL algorithms. The decentralized learning scheme and high sample efficiency also make it viable to large-scale power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Boundary-Aware Adversarial Learning Domain Adaption and Active Learning for Cross-Sensor Building Extraction

The use of convolutional neural networks (CNNs) for building extraction from remote sensing images has been widely studied and many public datasets have been made available for accelerating development of these CNN models. Yet adapting pretrained models at scale in real-world scenarios remains a challenging task. The main barrier is that certain new labels are still needed to compensate for domain shifting between the labeled data and new images that potentially cover new geographic locations or that are from a different sensor. In this article, we propose to add informatively labeled samples from a new image pool under the paradigm of active learning. To select the most useful samples based on model uncertainty, we first tackle the problem of uncalibrated uncertainty estimation due to distribution shifting by adapting feature extractors with boundary-based adversarial learning. Calibrated uncertainty is used as the query criterion in the active learning process, where the most uncertain samples are selected for annotation and included for model retraining. The proposed workflow was tested with three data pairs in which each workflow represents a scenario often encountered in real-world applications, including adapting pretrained models to new images collected with different sensors or to new geographic areas where appearances and types of buildings are very different. Compared to several baselines, including random sampling, temperature scaling (a well-known uncertainty calibration technique), different query strategies, and active domain adaptation methods, the proposed workflow shows that strategically querying a smaller set of samples for labeling achieves comparable or better building extraction performance. The proposed method reduces the number of labeled samples required to achieve sufficient model accuracy, thus significantly reducing hundreds of person-hours for labeled data creation. In addition, we include a few considerations when deploying this workflow in a GPU cluster that can be easily adapted to achieve operational building extraction model retraining.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Soft Actor Critic Based Volt-VAR Co-optimization in Active Distribution Grids

Modern distribution networks are undergoing several technical challenges, such as voltage fluctuations, because of high penetration of distributed energy resources (DERs). This paper proposes a deep reinforcement learning (DRL)-based Volt VAR co-optimization technique for reducing voltage fluctuations as well as power loss under high penetration of DERs. In addition, the proposed approach minimizes the operational cost of the grid. A stochastic policy optimization based soft actor critic (SAC) agent is proposed to configure the optimal set-points of the reactive power outputs of the inverters. The performance of the proposed model is verified on the modified IEEE 34- and 123-bus systems and compared with a base case scenario with no reactive supply by inverters, and a local droop control approach. The results demonstrate that the proposed framework outperforms the conventional droop control method in improving the voltage profile, minimizing the network power loss, and reducing grid operational cost.

—Distribution grids, deep reinforcement learning, ↗

DOE OSTI2019

Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High-Resolution Remote Sensing and Surface Geophysical Data

Our study aims to explore the microtopographic controls that dictate the heterogeneity of plant communities in a mountainous floodplain-hillslope system, using remote sensing and surface geophysical techniques. Working within a lower montane floodplain-hillslope study site (750 m × 750 m) in the Upper Colorado River Basin, we developed a new data fusion framework, based on machine learning and feature engineering, that exploits remote sensing optical and light detection and ranging (LiDAR) data to estimate the distribution of key plant meadow communities at submeter resolution. We collected surface electrical resistivity tomography data to explore the variability in soil properties along a floodplain-hillslope transect at 0.50-m resolution and extracted LiDAR-derived metrics to model the rapid change in microtopography. Furthermore, we investigated the covariability among the estimated plant community distributions, soil information, and topographic metrics. Results show that our framework estimated the distribution of nine plant communities with higher accuracy (87% versus 80% overall; 85% versus 60% for shrubs) compared to conventional classification approaches. Analysis of the covariabilities reveals a strong correlation between plant community distribution, soil electric conductivity, and slope, indicating that soil moisture is a primary control on heterogeneous spatial distribution. At the same time, microtopography plays an important role in creating particular ecosystem niches for some of the communities. Such relationships could be exploited to provide information about the spatial variability of soil properties. This highly transferable framework can be employed within long-term monitoring to capture community-specific physiological responses to perturbations, offering the possibility of bridging local plot-scale observations with large landscape monitoring.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Benchtop Autonomous Electrochemical Characterization System for Combinatorial Thin-Film Solid Oxide Electrodes

The design of materials for electrochemical energy conversion is complicated by a vast search space of candidate materials and multifaceted property requirements: multicarrier conductivity, stability, and catalytic activity are all necessary but rarely intersect. Although self-driving laboratories are rapidly rising to address such material optimization problems, the required infrastructure for integrated, large-scale robotic facilities can be cost-prohibitive. Here we develop and evaluate a closed-loop measurement system for efficient screening of proton-conducting oxide electrodes for ceramic fuel cells and electrolyzers, building on top of an existing benchtop instrument and integrating techniques for rapid impedance measurement and automated analysis. This system exemplifies a “minimum viable” self-driving implementation that can deliver substantial benefits with relatively simple infrastructure. Combinatorial thin-film microelectrode libraries are characterized with a recently developed joint time-domain and frequency-domain impedance measurement technique, which provides an order-of-magnitude acceleration relative to conventional impedance spectroscopy. The distribution of relaxation times is extracted from impedance data and analyzed without human intervention. These results feed an active learning and Bayesian optimization process that learns to predict electrochemical impedance as a function of material composition, measurement temperature, oxygen partial pressure, and electrical bias, which further reduces the screening time by tenfold with optimized experimental sequences. We apply this system to Ba⁡(Co,Fe,Zr,Y)⁢O 3−𝛿 combinatorial libraries and evaluate its effectiveness for learning material property trends and optimizing expensive-to-evaluate properties such as activation energy. This offers insights into key methodological aspects of practical autonomous experimentation, including surrogate model validation, cost-aware acquisition functions, and high-throughput data interpretation. Our results demonstrate the efficacy of the system for rapidly gathering information, but also highlight real-world experimental challenges of thin-film degradation and numerical instability in surrogate models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1996

The cerebellum: a neuronal learning machine?

Comparison of two seemingly quite different behaviors yields a surprisingly consistent picture of the role of the cerebellum in motor learning. Behavioral and physiological data about classical conditioning of the eyelid response and motor learning in the vestibulo-ocular reflex suggests that (i) plasticity is distributed between the cerebellar cortex and the deep cerebellar nuclei; (ii) the cerebellar cortex plays a special role in learning the timing of movement; and (iii) the cerebellar cortex guides learning in the deep nuclei, which may allow learning to be transferred from the cortex to the deep nuclei. Because many of the similarities in the data from the two systems typify general features of cerebellar organization, the cerebellar mechanisms of learning in these two systems may represent principles that apply to many motor systems.

Non-NASA Center↗

DOE OSTI2023

The impact of the $\text{WHIM}$ on the $\text{IGM}$ thermal state determined from the low- z Lyman $\alpha$ forest

At z ≲ 1, shock heating caused by large-scale velocity flows and possibly violent feedback from galaxy formation, converts a significant fraction of the cool gas (T ~ 10 4 K) in the intergalactic medium (IGM) into warm–hot phase (WHIM) with T > 10 5 K, resulting in a significant deviation from the previously tight power-law IGM temperature–density relationship, T = T 0 (P/$\bar{p}$) γ-1 ⁠. This study explores the impact of the WHIM on measurements of the low-z IGM thermal state, [T 0 , γ], based on the b–NH 1 distribution of the Ly α forest. Exploiting a machine learning-enabled simulation-based inference method trained on Nyx hydrodynamical simulations, we demonstrate that [T 0 , γ] can still be reliably measured from the b–NH 1 distribution at z = 0.1, notwithstanding the substantial WHIM in the IGM. To investigate the effects of different feedback, we apply this inference methodology to mock spectra derived from the IllustrisTNG and Illustris simulations at z = 0.1. The results suggest that the underlying [T 0 , γ] of both simulations can be recovered with biases as low as |Δlog(T 0 /K)| ≲ 0.05 dex, |Δγ| ≲ 0.1, smaller than the precision of a typical measurement. Given the large differences in the volume-weighted WHIM fractions between the three simulations (Illustris 38 percent, IllustrisTNG 10 percent, and Nyx 4 per cent), we conclude that the b–N H1 distribution is not sensitive to the WHIM under realistic conditions. Finally, we investigate the physical properties of the detectable Ly α absorbers, and discover that although their T and Δ distributions remain mostly unaffected by feedback, they are correlated with the photoionization rate used in the simulation.

79 ASTRONOMY AND ASTROPHYSICS↗