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DOE OSTI2024

SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics

We introduce adversarial learning methods for data-driven generative modeling of dynamics of nth-order stochastic systems. Our approach builds on Generative Adversarial Networks (GANs) with generative model classes based on stable m-step stochastic numerical integrators. From observations of trajectory samples, we introduce methods for learning long-time predictors and stable representations of the dynamics. Our approaches use discriminators based on Maximum Mean Discrepancy (MMD), training protocols using both conditional and marginal distributions, and methods for learning dynamic responses over different time-scales. We show how our approaches can be used for modeling physical systems to learn force-laws, damping coefficients, and noise-related parameters. Our adversarial learning approaches provide methods for obtaining stable generative models for dynamic tasks including long-time prediction and developing simulations for stochastic systems.

• Artificial intelligence (AI) / machine learning ↗

DOE OSTI2023

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure–property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9–76.2 mN/m) and speeds of sound (1009.7–1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure–property relationships in ILs may still be somewhat premature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Initial Implementation Of Machine Learning System for SRF Cavity Fault Classification at CEBAF

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is high power CW electron accelerator. It uses a mixture of two types of SRF modules, older, lower energy C20/C50 modules and newer higher energy C100 modules, arrayed in two anti-parallel linear accelerators. Accurately classifying the type of cavity faults is essential to maintaining and improving accelerator performance. Each C100 cryomodule contains eight 7-cell cavities. When a cavity fault occurs within a cryomodule, all eight cavities generate 17 waveforms each containing 8192 points. This data is exported from EPICS and saved for review. Analysis of these waveforms is time intensive and requires subject matter expertise (SME). SMEs examine the data from each event and label it according to one of several known cavity fault types. Multiple machine learning models have been developed on this labeled dataset with sufficient performance to warrant the creation of a limited machine learning software system for use by accelerator operations staff. This paper discusses the transition from model development to implementation of a prototype system.

Carpenter, A.↗

DOE OSTI2021

A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys

Abstract The class of 9–12% Cr ferritic-martensitic alloys (FMA) and austenitic stainless steels have received considerable attention due to their numerous applications in high temperature power generation industries. To design high strength steels with prolonged service life requires a thorough understanding of the long-term properties, e.g., creep rupture strength, rupture life, etc., as a function of the chemical composition and processing parameters that govern the microstructural characteristics. In this article, the creep rupture strength of both 9–12% Cr FMA and austenitic stainless steel has been parameterized using curated experimental datasets with a gradient boosting machine. The trained model has been cross validated against unseen test data and achieved high predictive performance in terms of correlation coefficient ( $$R^{2} > 0.98 $$ R 2 > 0.98 for 9–12% Cr FMA and $$R^{2} > 0.95 $$ R 2 > 0.95 for austenitic stainless steel) thus bypassing the need for additional comprehensive tensile test campaigns or physical theoretical calculations. Furthermore, the feature importance has been computed using the Shapley value analysis to understand the complex interplay of different features.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring

Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach that enables effective net-load monitoring capabilities at high-frequency with minimal additional equipment and cost. The proposed machine learning based solution provides accurate multiclass state predictions while operating at a faster timescale (able to provide a prediction for each 60- Hz ac cycle used in US power grid) without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states but also individual load operating power levels. A test bed with eight residential appliances is used for validating the NILM approach. Results show that the overall method has high accuracy, good scaling and generalization properties.

feature extraction↗

DOE OSTI2025

Advancements on Multi-Fidelity Random Fourier Neural Networks: Application to Hurricane Modeling for Wind Energy

Multi-fidelity approaches are emerging as effective strategies in computational science to handle otherwise intractable tasks like Uncertainty Quantification (UQ), training of Machine Learning (ML) models, and optimization, for expensive high-fidelity applications in which the amount of available simulations or data is limited. The main idea is simple: large datasets generated for low-fidelity approximations of the problem at hand are fused with a much sparser dataset for the target (high-fidelity) system. In this paper, we build on our recent success in designing random Fourier Neural Networks (rFNNs) [1] to target problems arising in wind energy applications and in particular problems of interest for hurricane modeling. In this context, data for the high-fidelity models are limited and lower fidelity alternatives are needed. In this work, we introduce a novel multi-fidelity training approach for our rFNNs and demonstrate its use on a simple verification problem and on a hurricane modeling problem in which high-fidelity data are generated via Large-Eddy Simulations (LES), while low-fidelity data are given by a mesoscale model. Initial results demonstrate how the multi-fidelity training approach can improve the quality of the resulting surrogate.

Fourier Neural Networks↗

DOE OSTI2020

Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants

The transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, technologies used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants (NPPs) are under evaluation. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval thus reducing the maintenance cost. This paper describes distributed high-temperature stable fiber sensors fabricated in optical fibers through a roll-to-roll laser direct writing process using femtosecond lasers. Using phase-sensitive optical time domain reflectometry, distributed acoustic and vibration sensors can be developed and deployed to critical components and systems in NPPs to perform active measurements with spatial resolution down to 0.5-meter throughout the piping systems. Complex acoustic and vibration signatures harnessed by distributed sensors are registered and analyzed by artificial intelligence algorithms for degradation detection and flaw identification. Piping elbows with machined-in flaws were instrumented with fiber sensors. High-spatial-resolution data were used to develop and validate machine learning algorithms, including both linear and nonlinear regression, and classification. Additionally, classification and sensor analysis were also performed for data analysis. The paper concludes with recommendations and future work on applications of machine learning enabled high-resolution fiber sensors for piping degradation monitoring in current or future NPPs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗

DOE OSTI2023

Driving Next-Generation Workflows from the Data Plane

We observe the emergence of a new generation of scientific workflows that process data produced at a sustained rate by scientific instruments and large scale numerical simulations. This data is consumed by multiple analysis, visualization, or Machine Learning components not only to enable inference and justify the scientific program, but also to monitor and steer the evolution of these experiments. In such workflows, moving intermediate data efficiently is key to performance, more than efficiently scheduling computational tasks. However, most traditional workflow management systems focus on optimizing task scheduling and then deal with data management, assuming a “move little, compute for long” model, which makes them unfit to the efficient management of this new generation of workflows. Therefore, we advocate for a new way to manage scientific workflows. We propose to consider an efficiently and independently managed data plane that can store and stream data. Workflows compute components, in the application plane can then interact with the data plane, abstracted from complexities of data management. Then, the role of a workflow management system would become that of a control plane that allows users to connect services together to execute the workflow and manages connections between the application and data planes. In this position paper, we characterize several next-generation workflow motifs and describe how their interaction with the data plane is a challenge to traditional workflow management systems. Then, we express a set of requirements that a workflow management system should meet to efficiently manage next-generation workflows at different scales. Based on these requirements, we expose our vision of driving next-generation workflows from the data plane and list remaining open challenges.

Suter, Fred↗

DOE OSTI2022

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Abstract Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Compressing unstructured mesh data from simulations using machine learning

The amount of data output from a computer simulation has grown to terabytes and petabytes as increasingly complex simulations are being run on massively parallel systems. As we approach exaflop computing in the next decade, it is expected that the I/O subsystem will not be able to write out these large volumes of data. In this paper, we explore the use of machine learning to compress the data before it is written out. Despite the computational constraints that limit us to using very simple learning algorithms, our results show that machine learning is a viable option for compressing unstructured data. Furthermore, we demonstrate that by simply using a better sampling algorithm to generate the training set, we can obtain more accurate results compared to random sampling, but at no extra cost. Further, by carefully selecting and incorporating points with high prediction error, we can improve reconstruction accuracy without sacrificing the compression rate.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks

Generative Adversarial Networks (GANs) are becoming popular choices for unsupervised learning. At the same time there is a concerted effort in the machine learning community to expand the range of tasks in which learning can be applied as well as to utilize methods from other disciplines to accelerate learning. With this in mind, in the current work we suggest ways to enforce given constraints in the output of a GAN both for interpolation and extrapolation. The two cases need to be treated differently. For the case of interpolation, the incorporation of constraints is built into the training of the GAN. The incorporation of the constraints respects the primary game-theoretic setup of a GAN so it can be combined with existing algorithms. However, it can exacerbate the problem of instability during training that is well-known for GANs. We suggest adding small noise to the constraints as a simple remedy that has performed well in our numerical experiments. The case of extrapolation (prediction) is more involved. First, we employ a modified interpolation training process that uses noisy data but does {\it not} necessarily enforce the constraints during training. Second, the resulting modified interpolator is used for extrapolation where the constraints are enforced after each step through projection on the space of constraints.

Stinis, Panagiotis↗

DOE OSTI2024

Insights into Prismatic Loop Formation in Irradiated Fe–Cr Alloys from Hypothesis-Driven Active Learning and Causal Analysis

Neutron and electron irradiation experimental studies conducted on body-centered cubic Fe and Fe–Cr alloys have established two prismatic dislocation loop populations, which have Burgers vectors of either a/2$\langle$111$\rangle$ or a$\langle$100$\rangle$. Here, the loop formation depends on factors such as dose (D), dose rate (D rt ), temperature (T), chromium content (Cr%), and other alloying elements. Hence, it is important to understand how irradiation-induced dislocation loops evolve conditional upon the loop characteristics, such as loop density (DD), average loop size d̅, and irradiation parameters (D, D rt , T, and irradiation type), which is still an active area of research. To understand these complex structure–property relationships, machine learning (ML) is employed in a three-step approach. This includes imputing missing data with a k-nearest neighbor, generating functionalized features, and assessing feature importance with random forest classification and regression. Physics-based features are incorporated in a hypothesis-driven active learning scheme to overcome data unavailability challenges. Insights obtained from ML models (i) to categorize dislocation loop types, show the highest correlation with d̅; (ii) Log(DD), obtained through mathematical formulations involving D, Cr%, d̅, and T (e.g., Log(DD) ~ D + exp(-Cr%) + 1/d̅ and log(DD) ~ D + exp(-Cr%) + 1/T). Hypothesis-driven active learning is able to predict Log(DD) in which the experimental date is not known. Causal models verify cause–effect relationships for dislocation loop classification and irradiation factors in FeCr alloys.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Hardware-accelerated inference for real-time gravitational-wave astronomy

The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Overall, our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2010

ArborZ: PHOTOMETRIC REDSHIFTS USING BOOSTED DECISION TREES

Precision photometric redshifts will be essential for extracting cosmological parameters from the next generation of wide-area imaging surveys. In this paper, we introduce a photometric redshift algorithm, ArborZ, based on the machine-learning technique of boosted decision trees. We study the algorithm using galaxies from the Sloan Digital Sky Survey (SDSS) and from mock catalogs intended to simulate both the SDSS and the upcoming Dark Energy Survey. We show that it improves upon the performance of existing algorithms. Moreover, the method naturally leads to the reconstruction of a full probability density function (PDF) for the photometric redshift of each galaxy, not merely a single 'best estimate' and error, and also provides a photo-z quality figure of merit for each galaxy that can be used to reject outliers. We show that the stacked PDFs yield a more accurate reconstruction of the redshift distribution N(z). We discuss limitations of the current algorithm and ideas for future work.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

A Machine Learning-based Approach to Predict the Aggregate Flexibility of HVAC Systems

Use of renewable energy resources can play a significant role in mitigation of climate change since they reduce the dependency on fossil fuels. However, high penetration of renewable energy resources into the power grid may cause some reliability problems. Ancillary services are key to realize the potential of renewable resources without disturbing the grid reliability. In recent years, many control approaches focused on exploiting the aggregate flexible heating, ventilation, and air-conditioning (HVAC) loads of buildings. However, there are still research gaps in assessing in advance the resulting indoor temperature responses for given total power and weather profiles before implementing the controls in the buildings. Such an assessment is critical to guarantee satisfying both the grid related objective (reliability) and the building related objective (occupants' comfort). Towards addressing this research gap, this paper presents a machine-learning approach for predicting the ratings of indoor temperature responses. In this approach, a prediction model was developed using a dataset generated by a set of model-free control simulations. The developed model achieved high prediction accuracy and showed the potential of the proposed approach.

Amasyali, Kadir↗

DOE OSTI2025

High-resolution leaf area index maps generated from unoccupied aerial system, Teller Mile 27, Seward Peninsula, Alaska

Leaf area index (LAI), a measure of the amount of one-side leaf area per ground unit, is an important indicator of plant carbon, energy, and water cycle. In the heterogeneous Arctic landscapes, it has been challenging to accurately measure LAI across species and space needed for Earth system model validation. Here, we use multispectral unoccupied aerial systems (UASs) to scale up and map leaf area index (LAI) , in a low-Arctic tundra landscape on the Seward Peninsula, Alaska. We linked previous published LAI measurements with high-resolution, UAS-collected multispectral data collected over the region of Next Generation Ecosystem Experiments in the Arctic (NGEE Arctic)’s Teller Mile Maker 27 site in 2022 to develop random forest (RF) machine learning models to predict and map LAI. 100 RF models were developed to account for uncertainties in ground LAI plot measurements and process scaling. This dataset includes a raster (*.tif) map of the mean LAI value of the 100 RF models, a raster (*.tif) map of the standard deviation of the RF-modeled LAI data, and a user guide (*.pdf).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl

Critical to many modern forensic investigations is the chemical attribution of the origin of an illegal drug. This process greatly relies on identification of compounds indicative of its clandestine or commercial production. The results of these studies can yield detailed information on method of manufacture, sophistication of the synthesis operation, starting material source, and final product. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3- methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods were studied in an effort to identify and classify route-specific signatures. These methods were chosen to minimize the use of scheduled precursors, complicated laboratory equipment, number of overall steps, and demanding reaction conditions. Using gas and liquid chromatographies combined with mass spectrometric methods (GC-QTOF and LC-QTOF) in conjunction with inductivelycoupled plasma mass spectrometry (ICP-MS), over 240 distinct compounds and elements were monitored. As seen in our previous work with CAS of fentanyl synthesis the complexity of the resultant data matrix necessitated the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 62 statistically significant, route-specific CAS were identified. Statistical classification models using a variety of machine learning techniques were then developed with the ability to predict the method of 3-methylfentanyl synthesis from three blind crude samples generated by synthetic chemists without prior experience with these methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗