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DOE OSTI2025

LCLS Big Data Handling – How I Learned to Stop Worrying and Love the Data Deluge

Advanced data and computing systems are vital to Linac Coherent Light Source (LCLS) operations, data interpretation and overall scientific productivity. The transition to MHz-era operation marks a fundamental change in scale that requires new infrastructure and architectures to link LCLS to the required scale of computing needed for scientific interpretation. The LCLS-II Data System meets big data challenges by implementing configurable data reduction that can adapt to multiple science areas, real-time analysis frameworks to provide visualization and fast feedback, and the ability to transfer data to local and remote computational facilities for near real time analysis at the appropriate scale. Feature extracted information generated in the data analysis pipeline - at the edge, local compute, or remote High-Performance Computing (HPC) resources - can be used to steer experiments and inform user decisions during beam time. Artificial Intelligence and Machine Learning (AI/ML) techniques present new opportunities to rapidly analyse large datasets and direct experiments, but create new challenges in scaling, adaptability, complexity, and trustworthiness. We describe how the LCLS-II Data System architecture addresses its data-driven challenges in the areas of data acquisition, data processing, data management, and workflow orchestration to decrease the overall time-to-science and provide a vision for future developments.

artificial intelligence↗

DOE OSTI2026

Towards a self-driving trigger at the LHC: adaptive response in real time

Real-time data filtering and selection—or trigger—systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Emami, Shaghayegh [Michigan U.] (ORCID:00090007589↗

DOE OSTI2025

Homomorphic data compression for real time photon correlation analysis

The construction of highly coherent X-ray sources, combined with next-generation detectors that are larger and faster, has enabled new research opportunities across the scientific landscape. Among the techniques that benefit most from these advancements is X-ray photon correlation spectroscopy (XPCS), where faster acquisition unlocks the ability to study faster dynamics within samples. However, faster acquisition on larger detectors also introduces unprecedented challenges for online data processing and offline data storage. Such challenges are particularly prominent for XPCS, where real time analyses require simultaneous calculation of all the previously acquired data in the time series. We present a homomorphic compression scheme to effectively reduce the computational time and memory space required for XPCS analysis. Leveraging similarities in the mathematical expression between a matrix-based compression algorithm and the correlation calculation, our approach allows direct operation on the compressed data without their decompression. The offline compression scheme extends storage capacity by a factor of 40 while preserving key features in the lossy compressed data. Meanwhile, the online compression scheme reduces the computational time to below 1 ms, enabling real time calculation of the correlation functions at kHz framerate. Our demonstration of a homomorphic compression of scientific data provides an effective solution to the big data challenge at coherent light sources. Beyond the example shown in this work, the framework can be extended to facilitate real-time operations directly on a compressed data stream for other techniques.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Improved damage tolerance of SiC-based nuclear fuel cladding with novel multi-layered SiC coating design at 1200 °C

Continuous SiC fibre reinforced SiC matrix composites (SiC f -SiC m ) with monolithic SiC outer coatings are considered as a damage-tolerant cladding design for loss of coolant accident (LOCA) conditions in light water reactors. However, monolithic SiC coatings are brittle and prone to catastrophic failure. In this study, a SiC f -SiC m cladding with a novel multi-layer SiC outer coating (11 sub-layers, ∼260 µm in total thickness) was investigated under C-ring compression at room temperature and 1200 °C in argon environment. Real-time synchrotron X-ray computed tomography (XCT) was employed to capture crack initiation and propagation processes. Compared to conventional monolithic SiC outer coatings, the multi-layer coating structure facilitated crack deflection and bifurcation enhancing its damage tolerance at both temperatures. Despite pre-existing surface cracks, claddings exhibited stable mechanical-performance at both temperatures. These initial cracks did not critically affect the failure processes as they were not aligned with the maximum stress direction. Furthermore, the microstructure, distribution of residual stresses, and local properties of individual components in the material were thoroughly characterized, and compared with open literature on conventional claddings with monolithic outer coatings. These results provide new insights into the failure mechanisms of multi-layer SiC coatings and offer guidance for the future design of accident-tolerant nuclear fuel claddings.

36 - MATERIALS SCIENCE↗

DOE OSTI2025

Visualizing degradation mechanisms in a gas-fed CO 2 reduction cell via operando X-ray tomography

We utilize operando X-ray computed tomography, coupled with real-time electrochemical analysis, to reveal the underlying failure mechanisms of membrane electrode assemblies (MEAs) for electrochemical CO 2 reduction (eCO 2 R). Through operando imaging, we can obtain unprecedented insights into the dynamic behavior of the MEA under different operating conditions, revealing critical changes in interface interactions, phase distribution, and structural integrity over time. Our findings identify phenomena giving rise to the transition from CO 2 R to the hydrogen evolution reaction (HER), as evidenced by shifts in cathode potential and CO 2 R selectivity. The formation of inhomogeneous precipitates at the gas diffusion electrode disrupts the CO 2 supply and reduces the active sites for eCO 2 R, resulting in a shift toward H2 production during low current density operation. Additionally, under high current density conditions, rapid water crossover up to the microporous layer/gas diffusion layer promotes the transition from CO 2 R to HER, further shifting cell potential toward anodic direction. Oscillating voltage conditions reveal the dissolution and regrowth of precipitates, providing direct visualization of the competing selectivity of CO 2 R and HER. This work offers new insight into the degradation mechanisms of MEAs, with implications for the design of more durable CO 2 R systems.

Lee, Sol A [California Institute of Technology (Ca↗

DOE OSTI2024

Hydra: Computer Vision for Online Data Quality Monitoring

Hydra is a system utilizing computer vision for near real-time data quality monitoring. Currently operational across all of Jefferson Lab’s experimental halls, it reduces the workload of shift takers by autonomously monitoring diagnostic plots during experiments. Hydra uses "off-the-shelf" supervised learning technologies and is supported by a comprehensive MySQL database. To simplify access, web apps have been developed to facilitate both labeling and monitoring of Hydra’s inferences. Hydra can connect with the alarm system and incorporates complete historical tracking, enabling it to identify issues that shift takers could miss. When issues are detected, a natural first question is: "Why does Hydra think there is a problem?" To answer, Hydra employs Gradient-weighted Class Activation Maps (GradCAM) to identify regions of the image that are important for the specific classification. This interpretive layer enhances transparency and trustworthiness, which is essential for integration with experiment workflows and operation. The Hydra system, results, and sociological considerations for deployment will be discussed.

Jeske, Torri↗

DOE OSTI2024

Hydra: computer vision for data quality monitoring

Hydra, initially developed for Hall-D in 2019, is a system that utilizes computer vision to perform near real time data quality monitoring. Since then, it has been deployed across all experimental halls at Jefferson Lab, with the CLAS12 collaboration in Hall-B being the first outside of GlueX to fully utilize Hydra. The system comprises back end processes that manage the models, their inferences, and the data flow. Finally, the front-end components, accessible via web pages, allow detector experts and shift crews to view and interact with the system.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

AI Driven Optimization of Public Transit

This project explores the application of AI-driven methods to optimize public transit operations for the Chattanooga Area Regional Transportation Authority (CARTA). By leveraging data analytics, machine learning, and predictive modeling, the initiative seeks to enhance system efficiency, improve rider experience, and support sustainability goals. This research, supported by the National Science Foundation and the U.S. Department of Energy, integrates real-time transit data with advanced computational tools to inform decision-making, optimize routes, and balance operational demands. The work exemplifies a forward-looking model for mid-sized cities aiming to modernize mobility systems through intelligent technology integration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

AI Applications to Physics Experiments at Jefferson Lab

We survey how AI/ML is being deployed across Jefferson Lab's experimental and accelerator programs. In EPSCI, Hydra applies computer vision to automate real-time data-quality monitoring across all four experimental halls, replacing manual inspection of hundreds to thousands of histograms per shift. AIEC (AI Experiment Controls) uses ML to stabilize drift chamber gains and is now part of standard CEBAF production running, while AI Optimized Polarization (AIOP) targets autonomous control of polarized targets and photon beam angular alignment. In CASA, cavity fault classification models identify faulted cavities and trip types from waveform data with ~85% and ~78% agreement to labeled data, respectively, and are deployed in production; a separate effort applies LLMs and hybrid search to make the CEBAF operations logbook AI-ready. QCD-focused work includes transformer- and GAN-based generative models for particle-level event simulation, with distributed GAN training scaling studies on Polaris. Additional efforts span ML-on-FPGA for the EIC and a new Data Science Department coordinating anomaly detection, uncertainty quantification, and HPC-scalable ML lab-wide. Collectively, these projects illustrate AI's growing role in improving efficiency across JLab's nuclear physics mission.

Mei, Xinxin [Thomas Jefferson National Accelerator↗

DOE OSTI2024

YOLO for Radio Frequency Signal Classification

Radio frequency signal classification plays a pivotal role in various applications, including spectrum management, wireless security, and cognitive radio. Extant signal classification methods require significant data throughput and are not multilabel. We propose a novel approach to radio frequency signal classification by leveraging the You Only Look Once (YOLO) object detection method. YOLO is a state-of-the-art deep learning model renowned for its real-time object detection capabilities in computer vision applications. We adapt YOLO for signal classification to enable the automatic and efficient identification of various signal types within a power spectral density image. Index Terms—radio-frequency analysis, object detection, neural networks, machine learning, deep learning.

42 ENGINEERING↗

DOE OSTI2026

Goal-oriented real-time Bayesian inference for linear autonomous dynamical systems with application to digital twins for tsunami early warning

We present a goal-oriented framework for constructing digital twins with the following properties: (1) they employ discretizations of high-fidelity partial differential equation (PDE) models governed by autonomous dynamical systems, leading to large-scale forward problems; (2) they solve a linear inverse problem to assimilate observational data to infer uncertain model components followed by a forward prediction of the evolving dynamics; and (3) the entire end-to-end, data-to-inference-to-prediction computation is carried out without approximation and in real time through a Bayesian framework that rigorously accounts for uncertainties. Several challenges must be overcome to realize this framework, including the large scale of the forward problem, the high dimensionality of the parameter space, and for a class of problems including those we target, the slow decay of the singular values of the parameter-to-observable map. Here we introduce a methodology to overcome these challenges by exploiting the autonomous structure of the forward model to decompose the solution of the inverse problem into a one-time-only offline phase in which the PDE model is solved a limited number of times (equal to the number of sensors), and an online phase that maps well onto GPUs and computes the parameter inference and prediction of quantities of interest in real time, given observational data. Our ultimate goal is to apply this framework to construct digital twins for subduction zones, including Cascadia, to provide early warning for tsunamis generated by megathrust earthquakes. To this end, we demonstrate how our methodology can be used to employ seafloor pressure observations, along with the coupled acoustic–gravity wave equations, to infer the earthquake-induced spatiotemporal seafloor motion (discretized with $\mathscr{O}$ (10 9 ) parameters) and forward predict the tsunami propagation. We present results of an end-to-end inference, prediction, and uncertainty quantification for a representative test problem with $\mathscr{O}$ (10 8 ) inversion parameters for which goal-oriented Bayesian inference is accomplished exactly and in real time, that is, in a matter of seconds.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

NLML: A Deep Neural Network Emulator for the Exact Nonlinear Interactions in a Wind Wave Model

Nonlinear wave interactions describe the resonant energy transfer between wave components, playing a fundamental role in the evolution of ocean wave spectra. Nonlinear wave interactions significantly influence wave growth and development, making them essential for accurate wave modeling. However, resolving the full six-dimensional Boltzmann integral of the exact nonlinear wave interactions (Webb-Resio-Tracy method, WRT) is computationally expensive, limiting its application in real-time operational wave forecasting and for research purposes. Current approximations, such as the Discrete Interaction Approximation (DIA), prioritize computational speed over accuracy, resulting in significant errors in wave mean parameters. Here, we introduce NLML, a machine learning (ML) emulator designed to approximate the exact nonlinear wave interactions within WAVEWATCH III (WW3), with the goal of achieving the accuracy of WRT while maintaining the stability and computational speed of DIA. By leveraging GPU capabilities such as half precision inference, we achieved substantial speedups, up to 136x mathematical equation faster than the WRT and only a modest 1.04x mathematical equation slowdown relative to DIA, while achieving 2x mathematical equation the accuracy of DIA in global wave spectral energy and mean wave parameters, with up to 7x mathematical equation higher accuracy in some regions. Unlike previous ML approaches, NLML maintained inherent stability throughout model integration in a standalone, year-long WW3 simulation, without requiring additional constraints. Our new ML parameterization bridges the gap between accuracy and efficiency, offering a promising alternative for improving wave modeling in operational settings and research purposes.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

Cross-correlation image analysis for real-time single particle tracking

Accurately measuring the translations of objects between images is essential in many fields, including biology, medicine, chemistry, and physics. One important application is tracking one or more particles by measuring their apparent displacements in a series of images. Popular methods, such as the center of mass, often require idealized scenarios to reach the shot noise limit of particle tracking and, therefore, are not generally applicable to multiple image types. More general methods, such as maximum likelihood estimation, reliably approach the shot noise limit, but are too computationally intense for use in real-time applications. These limitations are significant, as real-time, shot-noise-limited particle tracking is of paramount importance for feedback control systems. To fill this gap, we introduce a new cross-correlation-based algorithm that approaches shot-noise-limited displacement detection and a graphics processing unit-based implementation for real-time image analysis of a single particle.

Instruments & Instrumentation↗

DOE OSTI2024

ExaFEL: extreme-scale real-time data processing for X-ray free electron laser science

ExaFEL is an HPC-capable X-ray Free Electron Laser (XFEL) data analysis software suite for both Serial Femtosecond Crystallography (SFX) and Single Particle Imaging (SPI) developed in collaboration with the Linac Coherent Lightsource (LCLS), Lawrence Berkeley National Laboratory (LBNL) and Los Alamos National Laboratory. ExaFEL supports real-time data analysis via a cross-facility workflow spanning LCLS and HPC centers such as NERSC and OLCF. Our work therefore constitutes initial path-finding for the US Department of Energy's (DOE) Integrated Research Infrastructure (IRI) program. We present the ExaFEL team's 7 years of experience in developing real-time XFEL data analysis software for the DOE's exascale supercomputers. We present our experiences and lessons learned with the Perlmutter and Frontier supercomputers. Furthermore we outline essential data center services (and the implications for institutional policy) required for real-time data analysis. Finally we summarize our software and performance engineering approaches and our experiences with NERSC's Perlmutter and OLCF's Frontier systems. This work is intended to be a practical blueprint for similar efforts in integrating exascale compute resources into other cross-facility workflows.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Deployment and Evaluation of SciStream on OLCF's Advanced Computing Ecosystem (ACE)

The growing demand for real-time analysis, experimental steering, and decision-making in scientific workflows has created a need for tightly coupled integrations between experimental facilities and high-performance computing (HPC) systems. The Department of Energy’s Integrated Research Infrastructure (IRI) initiative highlights data streaming as a key capability for enabling memory-to-memory data transfers, bypassing the limitations of traditional store-and-forward models. SciStream is a toolkit developed by researchers at Argonne National Laboratory (ANL) to support such streaming by addressing cross-domain security, delegated authentication, and application transparency. We deployed and evaluated SciStream on the Oak Ridge Leadership Computing Facility’s (OLCF) Advanced Computing Ecosystem (ACE) infrastructure, leveraging the Olivine OpenShift cluster and its high-bandwidth Data Streaming Nodes (DSNs) as gateway nodes. Our evaluation included synthetic streaming workloads derived from IRI science workflows, a streaming simulator, and integration with RabbitMQ to handle low-level messaging. This report documents the deployment process, performance evaluation, and challenges encountered, along with opportunities for future improvements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Operator learning for energy-efficient building ventilation control with computational fluid dynamics simulation of a real-world classroom

Energy-efficient ventilation control plays an important role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD) simulations provide detailed and physically accurate representations of indoor airflow, their high computational cost limits their use in real-time building control. In this work, we present a neural operator learning framework that combines the physical accuracy of CFD with the computational efficiency of machine learning to enable building ventilation control with the high-fidelity fluid dynamics models. Our method jointly optimizes the airflow supply rates and vent angles to reduce energy use and adhere to air quality constraints. We train an ensemble of neural operator transformer models to learn the mapping from building control actions to airflow fields using high-resolution CFD data. This learned neural operator is then embedded in an optimization-based control framework for building ventilation control. Experimental results show that our approach achieves significant energy savings compared to maximum airflow rate control, rule-based control, as well as data-driven control methods using spatially averaged CO 2 prediction and deep learning–based reduced-order models, while consistently maintaining safe indoor air quality. These results highlight the practicality and scalability of our method in maintaining energy efficiency and indoor air quality in real-world buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML

Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem’s wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.

Alnajjar, Anees↗

DOE OSTI2026

Real-time observation of toroidal current redistributions induced by three-dimensional MHD phenomena triggering vertical displacement events in tokamak plasmas

Three-dimensional MHD instabilities, including edge-localized modes (ELMs) and internal reconnection events (IREs), have been observed to precipitate loss of vertical stability in tokamak plasmas, resulting in vertical displacement events (VDEs). This vertical destabilization can occur due to toroidal current redistributions and/or shape changes resulting from these phenomena. Using a recently introduced method for rapidly reconstructing the two-dimensional toroidal plasma current density profile in real-time, results are presented that demonstrate the specific current distribution changes that occur during ELMs (on KSTAR) and IREs (on MAST-U) that lead to loss of vertical control. The method most efficiently reconstructs the toroidal current density profile by doing so on a basis of principal components of historical profiles. These principal components isolate dominant current profile dynamics, improving interpretability, increasing speed, and reducing dimensionality of the profile computation. On KSTAR, this computation is executed in the real-time plasma control system at a rate of 10 kHz (limited by available CPU cycle times), allowing the current profile evolution to be assessed at several times over the course of each ELM event. Further, by incorporating the reconstructions into a novel vertical stability metric, the contribution of specific current profile dynamics to the loss of vertical stability can be assessed in real-time for VDE avoidance and improved understanding of the causal relationship between three-dimensional MHD phenomena and VDEs. The success of this method in approximating toroidal current density profiles from kinetic equilibrium reconstructions is also presented ($R^2=0.990$), along with its capability to produce other equilibrium quantities of interest in real-time at high time resolution.

edge-localized modes↗