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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

APSO-enhanced algebraic derivative estimation approach for real-time traffic flow prediction on critical road sections during wildfire evacuation

In rapid-onset disaster scenarios such as wildfires, evacuation traffic often significantly deviates from historical patterns, rendering conventional data-driven forecasting methods less effective. To address this challenge, we propose an improved algebraic derivative estimation (ADE) incorporating particle swarm optimization (PSO) for real-time traffic flow prediction. Our approach dynamically adjusts the ADE prediction time window at each step by minimizing a cost function based on the mean and variance of accumulated forecasting errors within the window, thereby balancing bias and variability. We evaluate the method using traffic data from the January 2025 California wildfires, focusing on key road segments critical for large-scale evacuations. The results demonstrate that our approach surpasses established machine learning and deep learning models—XGBoost, LSTM, and GRU—in predictive accuracy and maintains high computational efficiency. Notably, the proposed method eliminates the need for offline model training. Moreover, rapid PSO-based tuning enables real-time deployment, which provides a crucial advantage in scenarios where evacuation timings and road closures change dynamically. In conclusion, these findings highlight the benefits of the PSO-enhanced ADE framework for emergency traffic management, where rapid, data-sparse forecasts are essential for effective evacuation planning.

Algebraic derivative estimation↗
DOE OSTI2026

Bayesian Physics Informed Spatio-Temporal Network for Streamflow Data Imputation

Reliable reconstruction of incomplete streamflow records is critical for improving hydrological forecasting, flood preparedness, and water resource management. However, large observational gaps and uncertainties in governing physical parameters limit the accuracy of traditional statistical and machinelearning imputation frameworks. To address these challenges, we develop a Bayesian Physics-Informed Spatio-Temporal Network (BPI-STNet) that jointly captures spatial and temporal dependencies while enforcing hydrologic consistency through embedded physical constraints. The framework integrates a GraphSAGE-LSTM architecture to model spatial connectivity across gauges and temporal flow dynamics, coupled with a Bayesian update mechanism to estimate uncertain parameters in a simplified water-balance framework. Unlike conventional physics-informed networks that rely on sampling-based posterior estimation, BPI-STNet derives an analytic solution to the inverse problem, allowing closed-form Bayesian updates of uncertain parameters Λ={α,β,k} using Gaussian priors and likelihoods. Applied to daily observations from the Susquehanna River Basin (1980-2022), BPI-STNet achieves substantial improvements over a purely data-driven RGNN baseline, which reduced RMSE by 23 % and MAE by 9 %, and achieving an average NSE values up to 0.96. The results demonstrate that coupling Bayesian inference with physics-informed learning yields physically consistent, uncertainty-aware reconstructions that preserve the temporal persistence and statistical distribution of observed flows. The proposed framework establishes a generalizable paradigm for data-sparse hydrologic systems where both data fidelity and physical interpretability are essential.

Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗
DOE OSTI2025

Securing Smart Manufacturing: Detection of Cyber-Physical Attacks in CNC-Based Systems

As Industry 4.0 advances, the integration of computer numerical control (CNC) machines and advanced manufacturing technologies is transforming production into smart manufacturing systems that blend physical and digital processes as cyber-physical systems. However, this increased cyber-physical connectivity exposes manufacturing systems to cyber threats that can cause severe operational and financial disruptions. This paper presents a comparative study on cyber attacks and anomaly detection techniques in manufacturing, focusing on network traffic from CNC machines. The data extracted from network packets includes machine commands and control signals exchanged between the machine's interface and control system, crucial for maintaining operational integrity. We explore two types of cyber attacks, design modification and command injection, which pose substantial risks to CNC machine productivity and system integrity. Our investigation involves experiments on a real CNC system, highlighting the urgent need for effective detection mechanisms. To address these threats, we evaluate three anomaly detection methods: dynamic time warping (DTW), rolling average, and a deep learning, long short-term memory (LSTM) time-series-based autoencoder. Each is assessed for its effectiveness in identifying anomalous behaviors caused by the attacks. Our findings demonstrate the unique strengths and limitations of each detection technique, providing a deeper understanding of their applicability in realworld manufacturing environments. The comparative analysis indicates that while certain methods are highly effective against specific attack types, others offer broader applicability across different attacks. This study contributes to the accurate detection of anomalies in CNC machining processes, thereby enhancing the reliability and security of smart manufacturing systems against diverse cyber threats.

Williams, Bethanie [Tennessee Technological Univer↗
DOE OSTI2025

Computational Performance Bounds Prediction in Quantum Computing With Unstable Noise

Quantum computing has significantly advanced in recent years, boasting devices with hundreds of quantum bits (qubits), hinting at its potential quantum advantage over classical computing. Yet, noise in quantum devices poses significant barriers to realizing this supremacy. Understanding noise’s impact is crucial for reproducibility and application reuse; moreover, the next-generation quantum-centric supercomputing essentially requires efficient and accurate noise characterization to support system management (e.g., job scheduling), where ensuring correct functional performance (i.e., fidelity) of jobs on available quantum devices can even be higher-priority than traditional objectives. However, noise fluctuates over time, even on the same quantum device, which makes predicting the computational bounds for on-the-fly noise is vital. Noisy quantum simulation can offer insights but faces efficiency and scalability issues. Here, in this work, we propose a data-driven workflow, namely QuBound, to predict computational performance bounds. It decomposes historical performance traces to isolate noise sources and devises a novel encoder to embed circuit and noise information processed by a Long Short-Term Memory (LSTM) network. For evaluation, we compare QuBound with a state-of-the-art learning-based predictor, which only generates a single performance value instead of a bound. Experimental results show that the result of the existing approach falls outside of performance bounds, while all predictions from our QuBound with the assistance of performance decomposition better fit the bounds. Moreover, QuBound can efficiently produce practical bounds for various circuits with over 106 speedup over simulation; in addition, the range from QuBound is over 10× narrower than the state-of-the-art analytical approach.

Li, Jinyang [George Mason Univ., Fairfax, VA (Unit↗
DOE OSTI2026

Safe Deep Reinforcement Learning for Active Distribution System Model Predictive Control with EVs and DERs

The temporal and spatial mismatch between PV generation and electric vehicle (EV) charging and discharging may cause voltage violations in active distribution networks. Despite the widespread use of deep reinforcement learning (DRL) in power system optimization and control, it lacks guarantees on constraint satisfaction during both training and deployment. This paper proposes a Lagrangian-based safe DRL approach for model predictive control (MPC) of active distribution systems with large-scale integration of PVs, EVs, and energy storage systems (ESSs). A Transformer-LSTM time-series model is proposed to forecast EV charging demand, which is then formulated as a constraint to ensure charging requirements are met. Using this prediction, a Lagrangian-based safe soft actor-critic (SAC) framework is developed for real-time control in a three-phase unbalanced distribution system, enforcing voltage safety constraints while optimizing the cumulative net reward. By integrating the forecasting model with multi-period constraints, the proposed framework jointly coordinates PV systems, EV charging and discharging, and ESS scheduling within the MPC horizon. Numerical experiments on a modified IEEE 123-bus system with real-world data show that, under a high PV penetration scenario, the proposed method increases the net reward by 30.74% and reduces average voltage violations from 0.0011 p.u. to 0.0002 p.u. compared with standard SAC. Compared with the optimal power flow (OPF) approach, it achieves similar voltage security while yielding lower line losses. It also maintains real-time control capability, reducing operation latency to 53.21 ms per 15-minute control interval. The proposed method remains effective under varying PV/EV penetrations and load conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Risk-Aware Measurement Synchronization and Recovery for DSSE With Heterogeneous Data Sources

Power distribution systems are increasingly integrating heterogeneous sensors with varying data reporting rates and types, which pose challenges to achieving observability at the desired temporal resolution of distribution system state estimation (DSSE). Multisensor failures caused by extreme events exacerbate these issues, introducing substantial uncertainties into DSSE. This article proposes a novel solution to these challenges by ensuring high-resolution system observability despite heterogeneous data sources and multisensor failures. First, a deep learning architecture combining long short-term memory (LSTM) and graph convolutional network (GCN) is employed to synchronize meters with different reporting rates, aiming to achieve system observability. A random-walk-model-based approach is introduced to generate pseudo-measurements while properly characterizing their uncertainties under multisensor failures. Finally, a disaster-risk-informed observability metric (RiOM) is defined to quantify the uncertainty associated with state estimation results. The proposed framework offers deeper insights into the system observability on the fly compared with conventional analysis. The effectiveness of the framework is demonstrated on an IEEE standard test case and a large-scale real-world distribution feeder in mid-Minnesota in the U.S.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels

Here, in this work, a Variational Autoencoder (VAE)-based data-driven modeling framework is developed with the overarching goal of enabling fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space molecular representation. Chemical structure in the form of Simplified Molecular Input Line Entry System (SMILES) string is fed as input, encoded into the VAE latent space, and decoded back to the SMILES string using Long Short-Term Memory (LSTM) networks. Complexities of the VAE training loss function are thoroughly examined by varying the weightage (beta (𝜷) parameter) of the latent space regularization term, thereby assessing the balance between reconstruction accuracy and validity, and focusing on both accurate molecular structure reconstruction and latent space consistency. Two different strategies for 𝜷 variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing 𝜷 from 0 to a threshold value of 0.25 over 75 epochs. The generative VAE model can be readily coupled with Quantitative Structure–Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel promising fuels.

fuel design↗
DOE OSTI2024

Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes

A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such decomposed kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection thus becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian Smoothing Spline ANOVA kernel (BSS-ANOVA), coupled with fast Gibbs sampling in a fully Bayesian approach. It quickly and effectively limits the number of terms, yielding a method with competitive accuracies, training and inference times for tabular datasets of low feature set dimensionality. Theoretical computational complexities are O ( N P 2 ) in training and O ( P ) per point in inference, where N is the number of instances and P the number of expansion terms. The inference speed and accuracy makes the method especially useful for dynamic systems identification, by modeling the dynamics in the tangent space as a static problem, then integrating the learned dynamics using a high-order scheme. The methods are demonstrated on two dynamic datasets: a ‘Susceptible, Infected, Recovered’ (SIR) toy problem, along with the experimental ‘Cascaded Tanks’ benchmark dataset. Comparisons on the static prediction of time derivatives are made with a random forest (RF), a residual neural network (ResNet), and the Orthogonal Additive Kernel (OAK) inducing points scalable GP, while for the timeseries prediction comparisons are made with LSTM and GRU recurrent neural networks (RNNs) along with the SINDy package.

Hayes, Kyle↗
DOE OSTI2025

Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification" Willard et al. (2025).

This data release provides all data and code used in the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025)" to model stream temperature, evaluate, and assess results. The associated manuscript explores the effect of different ensemble construction techniques across different common machine learning (ML) architectures for predictions in unmonitored basins. Modeling was done using long short-term memory (LSTM), gated recurrent unit (GRU), temporal convolution network (TCN), and extreme gradient boosting (XGBoost) models, and stream site coverage spans 1362 locations across the conterminous United States. The ensemble construction techniques investigated include ensemble by random weight initialization, differing hyperparameters, different random subsets of training data, different subselections of input features, different architectures, and Monte Carlo Dropout. The data is organized into these items items:Code repository and data for the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025).Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code:- data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repositoryData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2025streamensembles,author = {Jared Willard and Charuleka Varadharajan},title = {Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification"},year = {2024},doi = {10.15485/2527393},publisher = {ESS-DIVE Repository},url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2527393}}MLA: Willard, Jared, et al. Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification". 2025. ESS-DIVE Repository, doi:10.15485/2448016.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"

This data package contains the associated data and scripts for Nagamoto, E., Ombadi, M., Ciulla, F. et al. Widespread drought-driven declines in streamflows and water quality in the Upper Colorado River Basin during 1998-2022. Commun Earth Environ 7, 734 (2026). https://doi.org/10.1038/s43247-026-03890-5. This purpose of this study was to investigate the impact of the 21st century drought on water quantity and quality at catchments throughout the Upper Colorado River Basin (UCRB). We used stream flow, water temperature, specific conductance, air temperature, precipitation, and catchment attribute data for over 200 sites in the UCRB, collected from the National Water Information System using Basin3D (Varadharajan, 2023), GAGESII (Falcone, 2010), and the Google Earth Engine. We identified years of severe drought between 1998 and 2022 using the Standardized Precipitation Evaporation Index (SPEI), then calculated the relative change percentage of the stream flow, water temperature, and specific conductance from drought versus non-drought years. We used the attribute information from GAGESII to investigate what physical traits of catchments are associated streamflow vulnerability (greater relative change) or resilience to drought. We used land cover data from the National Land Cover Database (USGS, 2024) to assess any changes to physical attributes that may not be represented in the static attributes information in GAGESII. To increase data availability, we modeled stream temperature using methods from Willard, 2023. While the study period is water years 1998 to 2022, the raw water quantity and quality data extends to 1950 and the meteorological data extends to 1980. The data and code can be downloaded via the UCRB_drought.zip. Within the zip, the files are organized as follows: - INPUTS: Contains all input data used in UCRB_Drought_Workflow.ipynb - OUTPUTS: Contains all intermediate data created from UCRB_Drought_Workflow.ipynb as well as final products including the calculated Standardized Evapotranspiration Index (SPEI) - climatic_variables: The code used to collect meteorologic data from Google Earth Engine - feature_importance: The code used for the catchment attributes analysis - preprocessing: Code used in UCRB_Drought_Workflow_Preprocessing.ipynb - pyeto: Code used in UCRB_Drought_Workflow_Preprocessing.ipynb - calculations: Code used in UCRB_Drought_Workflow_Impacts.ipynb - plotting: Code used in UCRB_Drought_Workflow_Impacts.ipynb - README.md - UCRB_Drought_Workflow_Preprocessing.ipynb: The code used to prep raw data for the analysis - UCRB_Drought_Workflow_Impact.ipynb: The code which uses the prepped raw data for analysis, and plots all figures - requirements_ucrb-drought_v2.yml: The requirements file to create a virtual environment and Jupyter Lab kernel to run the code The INPUTS folder is organized into the following major directories and sub-directories. The "RDC_WT_SC_RAW" folder contains raw data for streamflow, water temperature, and specific conductance in a ".h5" file. The "NLCD_RAW" folder contains ".csv" files with annual land cover percentages for counties within the UCRB. The "MET_RAW" folder contains a ".csv" file with monthly meteorological data (air temperature and precipitation) for the sites in the UCRB which was obtained from code in the climatic_variables folder. The "GAGESII" folder contains ".csv" files with physical catchment attribute variables for catchments across the country. The "WT_LSTM_data" folder contains ".csv" files with calculated WT (Willard, 2023) and the associated RMSEs. The "Upper_Colorado_River_Basin_Boundary" folder contains geographic data including a shapefile for plotting in the UCRB_Drought_Workflow.ipynb. The "RESERVOIRS_RAW" folder contains ".csv" files for each reservoir in the UCRB with daily reservoir storage. There are also two files in the INPUTS folder that have combined reservoir storage data and reservoir metadata. The OUTPUTS folder is organized into the following major directories and sub-directories. The "RDC_WT_SC_data" folder contains a folder "Water_year" with the associated cleaned data, metadata, and data availability information in ".csv" files, a folder "Median_Relchange" with the relative change comparing drought to non-drought years in ".csv" files, and a folder "Peak95_Min5_Relchange" that has ".csv" files for the relative change in peak (95th %) and minimum (5th %) variables. The "NLCD_data" folder contains the difference in land cover from the beginning to end of the study period and the percentage of the county that is within UCRB bounds can be found in Nagamoto et al (2025)). The "MET_data" folder contains separated monthly air temperature and precipitation data and the calculated PET in ".csv" files. The "SPEI_data" folder contains ".csv" files with calculated SPEI values (one restricted to the study period and the other with information from the entire MET data period). The "Paper_Tables" folder contains two ".csv" files containing site information and data availability and information about the GAGESII trait aggregated categories. The base directory includes the file “flmd.csv” for a list and description of all files and the file “dd.csv” for data dictionaries. Scripts for preprocessing, analysis, and figure generation are located in the associated GitHub repository found at [https://github.com/iNAIADS/drought-impacts/tree/develop/UCRB-drought]. UPDATE 1: Title and code file updated to match submitted manuscript 10-15-2025. UPDATE 2: Code and data files updated to match revised manuscript 3-4-2026. UPDATE 3: Code and data files updated to match revised manuscript 6-7-2026. ** NOTE: DD and FLMD have not been updated yet. UPDATE 4: Added associated Manuscript information and DD and FLMD have been updated. To cite this code, please use the following BibTeX: @misc{nagamoto2025drought, author = {Emily Nagamoto and Fabio Ciulla and Mohammad Ombadi and Jared Willard and Rosemary Carroll and Charuleka Varadharajan}, title = {Dataset: "Widespread Drought-driven Declines in Streamflows and Water quality in the Upper Colorado River Basin (1998-2022)"}, year = {2025}, doi = {10.15485/2551894}, publisher = {ESS-DIVE Repository}, url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2551894} }

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data from: "Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics"

This data package was generated to support the manuscript “Towards CONUS-Wide Machine Learning-Augmented Conceptually Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics.” It provides input files, model outputs, plotting data, scripts, notebooks, and documentation used to develop, evaluate, and reproduce Mass-Conserving Perceptron (MCP)-based hydrologic modeling experiments across 513 selected Catchment Attributes and Meteorology for Large-sample Studies in the United States (CAMELS-US) basins. The files are organized by modeling component and analysis purpose, including rainfall–runoff experiments, snow module experiments, coupled hydrologic-snow experiments, Long Short-Term Memory (LSTM) benchmark results, model skill metrics, initialization and epoch records, cell-state normalization files, Akaike Information Criterion (AIC)-based model comparison files, and data used to generate manuscript figures. Tabular files can be opened using standard spreadsheet software or Python/R data-analysis tools. Python scripts, Jupyter notebooks, and selected MATLAB scripts are included for model execution, postprocessing, plotting, and statistical analysis. Quality assurance and quality control were conducted through the source-data selection and modeling workflow. Meteorological forcing, streamflow, and static catchment attributes were derived from the CAMELS-US dataset, and snow water equivalent data were derived from the University of Arizona (UA) Snow Water Equivalent dataset. Selected basins and time periods were screened during the associated research workflow to avoid missing observations or poor-quality cases. Static geospatial features were processed primarily using Quantum Geographic Information System (QGIS) and Geospatial Data Abstraction Library (GDAL) workflows. Additional details are provided in the associated manuscript and documentation.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗
DOE OSTI2025

sPHENIX heavy flavor jet tagging studies in p+p at $\sqrt{s_{NN}}=200~GeV$

Heavy-flavor jets, which are initiated from heavy quarks, are ideal probes for studying flavor dependent parton energy loss. We report on the performance of jet flavor tagging using two Neural Network Machine Learning (ML) models: the Long Short-Term Memory (LSTM) model and an Attention-based Neural Network, in simulations of 200 GeV p + p collisions. The tagging performance of bottom quark initiated jets with both ML models surpasses that of the traditional cut-based method. Technical details, including sample and kinematic variable selections, the machine learning training and testing setup with parameter tuning, and outcome comparisons, will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels (Final Scientific/Technical Report)

The project, "Advanced Facility Design and AI/ML Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels," represents a significant advancement in nuclear fuel recycling technology. It integrates cutting-edge multimodal sensor fusion, machine learning (ML), and digital twin (DT) technologies to address challenges in material safeguarding, process optimization, and regulatory compliance for pyroprocessing facilities. This research has significantly enhanced the understanding of pyrochemical fuel recycling processes by developing innovative tools and methodologies. The Multimodal Safeguards Monitoring Unit (MSMU) combines electroanalytical techniques, Raman spectroscopy, and differential thermal analysis (DTA) to enable high-fidelity, near-real-time material accountancy measurements. Machine learning techniques, such as Long Short-Term Memory (LSTM) autoencoders, are utilized to detect anomalies in material balances and sensor data, improving the reliability of safeguards monitoring. Additionally, digital twin technology has been established to provide real-time system-level monitoring and diagnostics, integrating physics-based models with sensor data to optimize process safety and efficiency.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Foundational Dataset for Developing Large-Sample Stream Temperature Models in the Conterminous United States

This dataset provides inputs, evaluation results, and trained weights from a large-sample Long Short-Term Memory (LSTM) model designed to predict daily stream temperatures across unregulated river reaches in the conterminous United States (CONUS). It includes dynamic meteorological and hydrologic forcings, static physiographic attributes, and model outputs from cross-validation experiments spanning 300 basins. It supports reproducible modeling, direct application for new basins, and provides data suitable for integration with reservoir and river simulations under current and future climates. It contains two .zip files described below · RQ-AI_runs.zip: Model outputs from 10-fold cross-validation experiments, including observed and predicted daily stream temperatures, along with test performance metrics for water years 2017–2019. Two versions are included: 1. Model trained and validated using subbasin-area weighted dynamic features. 2. Model trained and validated using whole-basin area weighted dynamic features. · RQ-AI_inputs.zip: Collection of all formatted dynamic and static predictor datasets (meteorological, hydrologic, and physiographic features) used in model training and analysis. Detailed instructions and data structure is held at the following GitLab repository: https://code.ornl.gov/tempwise/training.

Gomez-Velez, Jesus [Oak Ridge National Laboratory ↗
DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗
DOE OSTI2024

Transformer Neural Networks with Spatiotemporal Attention for Predictive Control and Optimization of Industrial Processes

In the context of real-time optimization and model predictive control of industrial systems, machine learning, and neural networks represent cutting-edge tools that hold promise for enhancing dynamic modeling. This work presents a novel transformer neural network architecture for real-time optimization and model predictive control. This network design includes a modified attention mechanism inspired by positional embedding attention from vision transformers and task-specific modifications to the input-output structure of the transformer’s decoder stack. Experiments were conducted using data from a 450 MW coal-fired power plant to evaluate this approach's effectiveness. The transformer neural network was compared with conventional recurrent models, including GRU and LSTM. The transformer exhibited a 6% increase in the R-squared (R2) value of predictions and an 83% reduction in mean squared error (MSE). Computation time was also reduced by 84% compared to conventional recurrent models.

Gallup, Ethan R.↗
DOE OSTI2024

Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided.

Nunes, Pedro (ORCID:0000000180012172)↗
DOE OSTI2026

Xanthos-Lake Model Source Code

This repository contains the source code for Xanthos-Lake, a lake-modeling extension of the Xanthos framework that introduces a coupled lake component comprising the Xanthos-Lake Snow and Ice Model (xLSIM) and the Xanthos-Lake Water Balance Model (xLWBM). xLSIM is a basin-aware machine-learning model for lake snow, ice, and thermal conditions. It predicts monthly lake ice thickness, snow depth, snow-cover fraction, mixing-layer temperature, and lake ice fraction from meteorological forcing and lake surface-area information. It uses sequence-based deep-learning architectures, including Transformer and hybrid Long Short-Term Memory–Transformer (LSTM–Transformer) models, together with seasonal encoding, multi-lake learning, physical masking, and basin-level cryospheric and non-cryospheric classification. The training workflow uses Ray for scalable execution and includes optional Ray Tune hyperparameter optimization. Model predictions, observations, diagnostics, and feature-importance outputs are written in NetCDF. xLWBM is the water-balance component of the new lake framework. It simulates monthly lake storage, surface area, evaporation, inflow, outflow, and lake–groundwater exchange. It combines physical water-balance equations with calibrated bathymetric relationships, weir-based outlet flow, modified Penman open-water evaporation, groundwater head relaxation, Penman–Monteith snow and ice sublimation, and snow, ice, and thermal conditions supplied by xLSIM. The model calibrates lake parameters against satellite-derived surface-area data, using evaporation-based calibration where surface-area data are unavailable, and supports small, medium, and large lake classes. For large lakes, xLWBM is integrated with the managed-routing workflow so that lake storage and outflow interact directly with downstream river routing and reservoir operations. Together, xLSIM and xLWBM provide Xanthos with a coupled lake-modeling capability. xLSIM supplies the snow, ice, and thermal conditions that affect lake evaporation and snow- and ice-related water exchanges, while xLWBM translates those conditions into dynamic lake storage, surface area, evaporation, and discharge. In return, xLWBM supplies evolving lake surface area to xLSIM. This coupling enables Xanthos to represent lakes as active hydrologic components within basin-scale water-availability and routing simulations.

Machine Learning↗