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DOE OSTI2022

Progress on Associated-Particle Imaging Algorithms, 2022

The present work describes progress on developing imaging algorithms that use fast neutron signatures acquired using the associated-particle imaging (API) method. The present work complements ongoing work to develop neutron source and detector hardware to enable field inspection by investigating algorithms that are capable of discriminating among critical materials or extracting three-dimensional (3D) geometrical information from single-sided or transmission measurements. The present work is divided into three approaches: 1.Iterative reconstruction of inelastic gamma-ray emissions to perform 3D time-of-flight (TOF) imaging in a single view in either transmission or backscatter configurations. Iterative reconstruction enables image resolution better than the inherent TOF resolution. 2.Decomposition of registered neutron and x-ray radiographs into an assumed material list for each pixel in the image. 3.Material identification using full spectral analysis that includes the emergent neutron and gamma ray energies, times, and angles. Progress for each approach is summarized for fiscal year (FY) 2022.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Noise-Induced Transition in Optimal Solutions of Variational Quantum Algorithms

Variational quantum algorithms are widely considered a promising class of near-team algorithms that may demonstrate a practical quantum advantage in problems of scientific interest. One of the major obstacles to a scalable realization is the difficulty in optimizing the noisy cost function.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Identifying and Explaining Anomalous Activity in Surveillance Video with Compression Algorithms

The primary purpose of this document is to outline the progress made on the LDRD titled “Identifying and Explaining Anomalous Activity in Surveillance Video with Compression Algorithms” in FY22 and FY23. In this LDRD, we explored the usage of compression-based analytics to identify anomalous activity in video. We developed a novel algorithm, Spatio-Temporal N-Gram PPM (STNG PPM) that accounts for spatially and temporally aware anomalies in video. We extracted features using motions vectors from video as well as operating on the raw features. STNG PPM is comparable to many deep learning approaches but does not require specialized hardware (GPUs) to run efficiently. We also examine the evaluation metrics and propose novel measures addressing faults in the current evaluation measures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Energy Storage Participation Algorithm Competition Overview: Market simulation, divisions, stages, storage attributes, and scoring

The Energy Storage Participation Algorithm Competition (ESPA-Comp) aims to assess the performance of participants’ battery storage offer algorithms on their ability to maximize the value of battery storage resources under three different market designs: two-settlement, multi-settlement, and rolling horizon forward markets. This document describes the overall design of the competition at a high level.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Algorithms and file structures to extend and enhance liquid chromatography and ion mobility mass spectrometry workflows (CRADA Final Report)

The purpose of this project was to continue supporting customizations of algorithms and raw data file structures to enhance software workflows for liquid chromatography (LC), mass spectrometry (MS) and ion mobility mass spectrometry (IM-MS)-based protein and metabolite characterization. PNNL worked with Agilent to design, implement, evaluate, and demonstrate new algorithms and integrated them as functionalities into the PNNL-PreProcessor software. The project augmented PNNL’s capabilities to analyze complex proteomics and metabolomics samples. These capabilities are directly beneficial to DOE and PNNL efforts to characterize and analyze these compounds in microbial and plant communities. The project assisted Agilent in further developing improved instrument-software solutions combining liquid chromatography and ion mobility with mass spectrometry for widespread applications in life sciences and other fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Randomized Algorithms for Linear Solvers

Recently, randomized algorithms in numerical linear algebra, specifically those centered around random sketching, have gained traction in primarily theoretical research due to their potential to significantly reduce problem dimensionality at the cost of an O(1) multiplicative distortion factor. It has been assumed that this sketching can be done efficiently, but thorough investigation into how precisely to do it has been neglected. Moreover, the theory-based community has argued for sketching’s ability to reduce computational cost via complexity analysis, but has not researched how it affects the stability of the algorithms. At Sandia, efficient linear solvers that scale well on modern HPC architectures while maintaining stability are imperative for practical applications. In this LDRD, we developed a random sketching strategy that is substantially faster than existing ones, and demonstrate its superior performance in practice on a NVIDIA H100 GPU. Moreover, we show how this can be used to significantly outperform existing linear least squares solvers while improving the solver’s stability as well. Additionally, we demonstrate how this sketching strategy can be used to make a fast, stable QR factorization that can subsequently be used in s-step and block Krylov solvers. Finally, we incorporate a sketching-based block orthogonalization scheme into s-step GMRES, which is stable and faster than existing approaches on the Perlmutter supercomputer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Radio Frequency Algorithms

Final Technical Report for DOE SC0019287 award titled Radio Frequency Algorithms. This project supported the design, simulation, analysis, and optimization of robust algorithms to configure, optimize, and control Radio Frequency and Low-Level RF systems for several modern accelerator systems.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Initial Refinement and Optimization of the Image Reconstruction (U-MBIR) Algorithm to Optimize its Performance in Detecting Damage and Flaws

Concrete is a critical component of nuclear power plants; thus, its safety and reliability must be thoroughly examined throughout the life cycle of the structural system. During the life cycle of this infrastructure, monitoring of the concrete for signs of degradation should be performed through nondestructive evaluation (NDE). Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. As such, Oak Ridge National Laboratory (ORNL) is developing advanced image reconstruction algorithms to overcome the limitations of traditional ultrasonic NDE methodologies. The results and discussion presented herein summarize the current state of the ultrasonic model–based iterative reconstruction (U-MBIR) algorithm developed at ORNL.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Fundamental Algorithmic Research for Quantum Computing (FAR‐QC) (Final report)

This document is the final technical report for the "Fundamental Algorithmic Research for Quantum Computing" (FAR-QC) project at Dartmouth College (PI: J. Whitfield, co-PI: L. Viola). It details the project's primary scientific accomplishments from 2019 to 2025, focusing on advances in quantum simulation algorithms, resource-efficient fermionic encodings, bosonic topology, and optimization methods for near-term quantum devices. The report also summarizes project impacts, including software development (Quiqbox.jl), workforce training, and a complete list of resulting publications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Selection Algorithm Improvement for MicroBooNE

Data selection is an extremely important part of data analysis for any experiment. Finding a physics result is often the result of sifting through a massive amount of data, keeping data that we believe to be signal and throwing out data we do not. This process is called data selection. Creating a selection algorithm is an intensive process that must balance keeping enough data to have statistics and maximizing the signal purity of that data. In this study, we used three different reconstruction tools, Pandora, WireCell, and LANTERN, for the MicroBooNE experiment in conjunction to improve the selection algorithm for analysis. For the case of this study, we look into the charged current N proton 0 pions (CCNp0$\pi$) interaction channel. This is the dominant channel for the Short Baseline Neutrino (SBN) program and is expected to be a large contributor to the Deep Underground Neutrino Experiment (DUNE). We first investigated each of the three tools to find out more about their strengths and weaknesses as reconstructions. We then put together a direct comparison of the three methods to find which method or combination of methods would return the best result for us. While the study is ongoing, we have learned a lot about data selection for the experiment and the differences between the reconstruction tools.

Dillon, Brayden [Michigan State U.]↗

DOE OSTI2026

Frameworks, Algorithms, and Scalable Technologies for Mathematics (FASTMath) SciDAC Institute

As computational models scale to larger computers, the rate at which they produce data has far outstripped the same computers ability to write that data and further the file systems ability to store that data. Almost all of the SciDAC applications, but especially those related to fusion solve very large scale PDEs whose scientific output his impacted by this problem. To gain access to dynamics in an exascale simulation that are not identifiable a priori and to make that dynamical data available to machine learning requires fundamental research in the area of in situ data data analytics. Here data analytics includes compression, visualization, uncertainty quantification, and machine learning. This in situ data analytics will enable on-the-fly spatial and temporal compression of solution dynamics, expose that space-time compressed field to machine learning algorithms that have been specialized to work with dynamically evolving data (existing machine learning algorithms treat data sets as static), greatly improving the opportunity for machine learning to provide feedback to the compression, all within an ongoing simulation, without the need to write data to files. The same concepts are also being applied to uncertainty quantification and multi-fidelity modeling which have similar needs for spatial and temporal compression of the ongoing exascale simulation to perform either without the typical, unacceptable writing of data to files.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

A large-scale benchmarking of deterministic and stochastic derivative-free optimization algorithms

This presentation summarizes our work in the PrOMMiS project on benchmarking of data-driven optimization algorithms and their applications in self-driving laboratories. This work supports the broader project goal of accelerating the identification of promising separation methods and operating conditions for critical minerals separation processes. We present a systematic benchmarking study of 42 data-driven optimization algorithms on a broad collection of 502 test problems. The results identify BAM, GLCCLUSTER, and MULTIMIN as the most effective optimization solvers, with BAM showing the highest overall performance and solving more than 80% of the benchmark problems. The study also shows that no single solver consistently outperforms the others across all problem types, indicating that our future laboratory applications may benefit from using a small set of strong solvers rather than relying on a single method. The presentation also illustrates an in-silico chemical reactor case study showing that data-driven optimization methods can guide autonomous experimentation in a self-driving laboratory and identify optimal operating conditions within a small number of experiments. Overall, the results provide a basis for selecting efficient optimization methods and demonstrate the practical use of data-driven optimization in self-driving laboratory workflows.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Algorithm-guided experimentation for autonomous AI systems in self-driving laboratories

36 MATERIALS SCIENCE↗

DOE OSTI2026

Hardware Implementation of Grover's Search Algorithm

Grover's algorithm searches through an unstructured database, offering a quadratic speedup over classical search algorithms. We implement it, as well as two deterministic variants, on IBM (Kingston) and IQM (Garnet) hardware. Additionally, we test dynamical decoupling as an error mitigation technique. We compare our results to a classical, brute force approach to evaluate current hardware capabilities.

Pressman, Daniel [Fermilab]↗

DOE OSTI2021

Application of Monte Carlo Algorithms to Cardiac Imaging Reconstruction

Monte Carlo algorithms have a growing impact on nuclear medicine reconstruction processes. One ofthe main limitations of myocardial perfusion imaging (MPI) is the effective mitigation of the scattering component,which is particularly challenging in Single Photon Emission Computed Tomography (SPECT). In SPECT,no timing information can be retrieved to locate the primary source photons. Monte Carlo methods allow anevent-by-event simulation of the scattering kinematics, which can be incorporated into a model of the imagingsystem response. This approach was adopted in the late Nineties by several authors, and recently took advantageof the increased computational power made available by high-performance CPUs and GPUs. These recent developmentsenable a fast image reconstruction with improved image quality, compared to deterministic approaches.Deterministic approaches are based on energy-windowing of the detector response, and on the cumulative estimateand subtraction of the scattering component. In this paper, we review the main strategies and algorithms tocorrect the scattering effect in SPECT and focus on Monte Carlo developments, which nowadays allow the threedimensionalreconstruction of SPECT cardiac images in a few seconds.

Pharmacology & Pharmacy↗

DOE OSTI2024

Multi-track reconstruction algorithm in the Mu2e experiment

The Mu2e experiment, under construction at Fermilab, will search for the neutrino-less coherent $\mu^-N\rightarrow e^-N$ conversion in the field of a $^{27}$Al nucleus. Such a process violates lepton flavor conservation. About $60\%$ of muons stopped by an $^{27}$Al nucleus will undergo nuclear capture, while about $40\%$ will decay in orbit. To quantify the conversion probability, we define $R_{\mu e}$, which is given by the ratio between the $\mu^-\rightarrow e^-$ conversion rate and the nuclear capture rate [1]: \begin{equation} R_{\mu e}= \frac{\Gamma\left(\mu^- + N\left(Z,A\right)\rightarrow e^- + N\left(Z,A\right)\right)}{\Gamma\left(\mu^- + N\left(Z,A\right)\rightarrow \nu^-_\mu + N\left(Z-1,A\right)\right)}\,. \end{equation} The upper limit on $R_{\mu e}$ is $7\cdot 10^{-13}$ at $90\%$ CL, set by the SINDRUM II experiment~\cite{SINDRUM II:limit}. The goal of the Mu2e experiment is to reach a sensitivity on $R_{\mu e}$ of $8\cdot 10^{-17}$ at $90\%$ CL. This represents a four-order of magnitude improvement over the current experimental limit. Mu2e will take its first data in 2027. The signature for the muon conversion is a monochromatic electron of $104.97$~\si{\mega\eV}/c, an energy slightly below the muon rest mass. While the main experiment goal is to reconstruct the conversion electron, i.e., an event with a single track, there are motivations to develop an efficient tracking algorithm for reconstructing more simultaneous tracks. This could better constrain the background generated by $p\bar{p}$-annihilation in the Al target and to search for other Beyond the Standard Model processes. In this paper, we present an algorithm designed to reconstruct multi-particle events.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Efficient Hamiltonian encoding algorithms for extracting quantum control mechanism as interfering pathway amplitudes in the Dyson series

Hamiltonian encoding is a methodology for revealing the mechanism behind the dynamics governing controlled quantum systems. In this paper, following Mitra and Rabitz \cite{abhra_1}, we define mechanism via pathways of eigenstates that describe the evolution of the system, where each pathway is associated with a complex-valued amplitude corresponding to a term in the Dyson series. The evolution of the system is determined by the constructive and destructive interference of these pathway amplitudes. Pathways with similar attributes can be grouped together into pathway classes. The amplitudes of pathway classes are computed by modulating the Hamiltonian matrix elements and decoding the subsequent evolution of the system rather than by direct computation of the individual terms in the Dyson series. The original implementation of Hamiltonian encoding was computationally intensive and became prohibitively expensive in large quantum systems. This paper presents two new encoding algorithms that calculate the amplitudes of pathway classes by using techniques from graph theory and algebraic topology to exploit patterns in the set of allowed transitions, greatly reducing the number of matrix elements that need to be modulated. These new algorithms provide an exponential decrease in both computation time and memory utilization with respect to the Hilbert space dimension of the system. To demonstrate the use of these techniques, they are applied to two illustrative state-to-state transition problems.

Abrams, Erez [Princeton University, Massachusetts ↗

DOE OSTI2016

Making Advanced Scientific Algorithms and Big Scientific Data Management More Accessible

Synchrotrons such as the Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory are known as user facilities. They are sources of extremely bright X-ray beams, and scientists come from all over the world to perform experiments that require these beams. As the complexity of experiments has increased, and the size and rates of data sets has exploded, managing, analyzing and presenting the data collected at synchrotrons has been an increasing challenge. The ALS has partnered with high performance computing, fast networking, and applied mathematics groups to create a"super-facility", giving users simultaneous access to the experimental, computational, and algorithmic resources to overcome this challenge. This combination forms an efficient closed loop, where data despite its high rate and volume is transferred and processed, in many cases immediately and automatically, on appropriate compute resources, and results are extracted, visualized, and presented to users or to the experimental control system, both to provide immediate insight and to guide decisions about subsequent experiments during beam-time. In this paper, We will present work done on advanced tomographic reconstruction algorithms to support users of the 3D micron-scale imaging instrument (Beamline 8.3.2, hard X-ray micro-tomography).

97 MATHEMATICS AND COMPUTING↗