Creating simple, interpretable anomaly detectors for new physics in jet substructure
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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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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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Fast point cloud generation with diffusion models in high energy physics
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Breaking the curse of dimensionality: Solving configurational integrals for crystalline solids by tensor networks
Accurately evaluating configurational integrals for dense solids remains a central and difficult challenge in the statistical mechanics of condensed systems. Here, we present a tensor network approach that reformulates the high-dimensional configurational integral for identical-particle crystals into a sequence of computationally efficient summations. We represent the integrand as a high-dimensional tensor and apply tensor-train (TT) decomposition together with a custom TT-cross interpolation. This approach circumvents the need to explicitly construct the full tensor. We introduce tailored rank-1 and rank-2 schemes optimized for sharply peaked Boltzmann probability densities, typical for identical-particle crystals. When applied to the calculation of internal energy and pressure-temperature curves for crystalline Cu and Ar at high (GPa) pressures, as well as the alpha-to-beta phase transition diagram of Sn, our method accurately reproduces molecular dynamics simulation results using tight-binding, machine learning, hierarchical interacting particle–neural network, and modified embedded atom method potentials,all within seconds of computation time.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Neural refinement of sample weights
Monte Carlo simulations are an essential tool in particle physics data analysis. Events are typically generated alongside weights that redistribute the cross section of the simulated process across the phase space. These weights can be negative, and several post hoc methods have been developed to eliminate or mitigate the negative values. All of these methods share the common strategy of approximating the average weight as a function of phase space. We introduce an alternative approach, which, instead of reweighting to the average, refines the initial weights with a scaling transformation, utilizing a phase space-dependent factor. Since this new refinement method does not need to model the full weight distribution, it can be more accurate. High-dimensional and unbinned phase space is processed using neural networks for the refinement method. In addition to the refinement method, we introduce a new resampling protocol, which can be used in conjunction with any weight transformation to not only preserve the average weight but also the statistical uncertainties of the initial distribution. Using both realistic and synthetic examples, we show that the new neural refinement method is able to match or exceed the accuracy of similar weight transformations and that the new resampling protocol is simpler in implementation than previous methods while exhibiting equivalent statistical properties.
Artificial neural networks↗
Deep neural networks in real-time coherent diffraction imaging
Abstract not provided.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Feature Engineering and Ensemble Methods for Imbalanced ICS Intrusion Detection: Pipeline Audit and Constrained Evaluation
Industries are becoming increasingly connected and are more vulnerable to cyberattacks due to the widened attack surface. Industrial Control Systems (ICS) are among the most critical sectors that malicious actors can target, as such attacks can cause significant operational disruption and physical damage. It is imperative to detect such attacks as early as possible. This paper evaluates constraint-conditioned optimistic performance estimates for traditional ML models in ICS intrusion detection (i.e., estimates obtained under contiguous, non-shuffled temporal evaluation without test-set alteration, but with pre-split feature engineering that may introduce temporal leakage, due to dataset constraints). Our findings are threefold. First, we quantify how iterative feature engineering affects tree-based ensemble performance and examine how pipeline decisions (split strategy, sampling scope, and cleaning policy) can inflate or reduce reported IDS results under constraint-bound evaluation. Second, we compare intrinsic class-imbalance handling across ensemble models. Third, under our current pipeline constraints (including pre-split feature engineering), CatBoost achieves the best performance on Water Storage Tank (accuracy: 0.9831, class-1 F1: 0.9682), while Light- GBM achieves the best performance on Gas Pipeline (accuracy: 0.9618, class-1 F1: 0.9086).
97 MATHEMATICS AND COMPUTING↗
Virtual Neuron: A Neuromorphic Approach for Encoding Numbers
Neuromorphic computers perform computations by emulating the human brain and are expected to be indispensable for energy-efficient computing in the future. They are primarily used in spiking neural network-based machine learning applications. However, neuromorphic computers are unable to preprocess data for these applications. Currently, data is preprocessed on a CPU or a GPU-this incurs a significant cost of transferring data from the CPU/GPU to the neuromorphic processor and vice versa. This cost can be avoided if preprocessing is done on the neuromorphic processor. To efficiently preprocess data on a neuromorphic processor, we first need an efficient mechanism for encoding data that can lend itself to all general-purpose preprocessing operations. Current encoding approaches have limited applicability and may not be suitable for all preprocessing operations. In this paper, we present the virtual neuron as a mechanism for encoding integers and rational numbers on neuromorphic processors. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware and show that it can perform an addition operation using 23 nJ of energy on average using a mixed-signal, memristor-based neuromorphic processor. The virtual neuron encoding approach is the first step in preprocessing data on a neuromorphic processor.
Date, Prasanna↗
Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems
Transient stability assessment (TSA) is a cornerstone for resilient operations of todays interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. Here, we devise a quantum TSA (QTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth (HELD) quantum circuit is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD circuit training; 3) A systematical analysis on QTSAs performance under various quantum factors is per-formed. QTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
CEGANN: CRYSTAL EDGE GRAPH ATTENTION NEURAL NETWORK
SF-22-156 Machine learning (ML) models and applications in materials design and discovery typically involve the use of feature representations or descriptors followed by a learning algorithm that maps them to user desired properties of interest. Most popular mathematical formulation-based descriptors are not unique across atomic environments and suffer from transferability issues across different application domains and/or material classes. The CEGANN code provides a unified interface to facilitate material characterization across materials across multiple scales (from atomic to mesoscale) and diverse classes of materials ranging from metals oxides, non-metals, and even hierarchical materials such as zeolites and semi ordered materials such as mesophases. CEGANN implements a Graph Attention Network (GAT) type convolution architecture. The details of network architecture can be found in the paper https://doi.org/10.48550/arXiv.2207.10168. The software comes with pretrained examples and dataset for the classification of the following representative systems: (1) Structure-level representation such as space group (2) Structural dimensionality (e.g., bulk, 2D, clusters etc.) (3) Grain boundary identification (4) Nucleation and growth of a zeolite polymorph (5) Characterization of binary mesophases and their phase transitions (6) Growth of ice. The code is written in python programming language.
CHAN, HENRYT↗
Machine learning of factors for improving oyster hatchery production
Oyster aquaculture and restoration in the Chesapeake Bay are vital, yet hatcheries frequently struggle with inconsistent larval growth and sudden mass mortality events. Unpredictable disruptions in larval production cause large economic losses, represent a perceived risk to growers, and impede industry expansion. To better understand associations between production yield and its potential predictors, we applied machine learning (random forest, and neural network) and statistical (generalized additive model) models to a comprehensive dataset of environmental, water quality, and operational parameters from a Maryland oyster hatchery, aiming to identify key yield predictors and develop a robust forecasting tool. We used recursive Boruta algorithm for variable selection, pinpointing critical predictors, and employed cross-validation to fine-tune model settings. Shapley value analysis offered crucial insights into model interpretations, highlighting week number, Normalized Difference Vegetation Index, salinity, turbidity, and fecundity as primary drivers of yield variability. For low-yield cases, salinity-related variables were particularly important. Our findings provide an early warning system for potential production downturns, empowering hatchery operators to make data-driven decisions for optimizing water conditions, feeding schedules, and broodstock management. By boosting predictability and efficiency, this research directly supports economic stability of the oyster industry and ecological health of the Chesapeake Bay.
Vishwakarma, Srishti [Oak Ridge National Laborator↗
Data and Models: A Framework for Advancing AI in Science
On June 5, 2019, the Office of Science (SC) organized a one-day roundtable to focus on enhancing access to high-quality and fully traceable research data, models, and computing resources to increase the value of such resources for artificial intelligence (AI) research and development and the SC mission.1 In this report, we consider AI to be inclusive of, for example, machine learning (ML), deep learning (DL), neural networks (NN), computer vision, and natural language processing (NLP). We consider “data for AI” to mean the digital artifacts used to generate AI models and/or employed in combination with AI models during inference. In part, this roundtable was motivated by the recognition that a large portion of science data currently are not well suited for AI.
97 MATHEMATICS AND COMPUTING↗
LCOE reduction through proactively optimized monitoring of PV Systems (Final Technical Report)
The project demonstrates the value proposition for a high-resolution monitoring system (HRMS) with diagnostic-prognostic capability and determine its impact on LCOE. The HRMS differs from conventional monitoring systems in a number of ways. First it will include the capability to automatically measure IV curves at the string and module levels. This provides a much richer view into the DC performance of the PV system and allows classification of many typical failure and degradation modes. Second, it will incorporate software capable of quantifying power and energy losses in the field as well as define the location and mechanism of the power and energy loss. Moreover, we aimed to deliver a prognostic system that is capable of predicting certain failures before they occur and giving system operators the opportunity to more efficiently plan operations and maintenance (O&M) activity in order to lower costs and increase yield over the life of the system. The research identifies cost targets required for different monitoring stages, including at the string combiner, at the individual string, and at the module level, to lower the levelized cost of energy (LCOE). Finally, a comprehensive guide determines the value PV monitoring brings to PV field operations. The guide assists plant operators in maximizing value from existing plants and identify the trade-offs of different monitoring solutions for future plants depending on the size, location, and expected system lifetime.
14 SOLAR ENERGY↗
An Application of Faster R-CNNs in Object Detection Efforts Applied to the Critical Infrastructure Domain
Object detection and segmentation approaches have been successfully applied to multiple interdisciplinary domains ranging from medical imaging to autonomous vehicles. The following study details efforts to apply segmentation methods to the critical infrastructure domain. Specifically, the identification of critical sub-components of four facilities: airports, hospitals, hydroelectric dams, and water treatment plants. Data for this study was extracted from the U.S. Department of Agriculture’s National Agriculture Imagery Program data set. Each facility class was considered independently of the other facilities, producing four different segmentation models during the testing and training process. Ultimately, the study team concluded there are significant challenges associated with object detection modeling when applied to facility analysis within the critical infrastructure domain. The study team was able to identify possible sources of these challenges that could be potentially addressed in future work.
97 MATHEMATICS AND COMPUTING↗
Enhanced Ion Detection for Atomic Mass Spectrometry
In atomic mass spectrometry, polyatomic interferences that obscure ions of interest are a serious concern and in many cases hamper, or make impossible, the measurement of low signal analytes. Electron multipliers in pulse counting mode are used prolifically when low intensity ion beams are present during analysis as they provide on the order of 106-107 signal amplification, however simply counting the number of electron pulses ignores differences in the impact physics that are present depending on ion identity. The objective of this research was to investigate the efficacy of utilizing advanced neural network analysis and machine learning to recognize the differences in the analog electron multiplier pulse shapes for atomic vs. polyatomic ions of similar or identical mass as a means of distinguishing the two ion types. In this work, we’ve successfully discriminated polyatomic interferants from atomic analytes of interest for two different mass spectrometers, and three different ion pairs; Krypton isotope/hydride and methane/oxygen (noble gas mass spectrometer) and 129I atomic vs 129I polyatomic (Triton mass spectrometer). Discrimination accuracy for these experiments ranged from 76% to 95%.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Cosmology constraints from Strong Gravitational Lensing using Simulation-Based Inference
In this poster we present our preliminary analysis on constraining cosmology parameters such as the Dark Energy Equation of State parameter using strong lensing. We use Simulations based inference with Machine Learning algorithms such as Neural Posterior density estimators.
79 ASTRONOMY AND ASTROPHYSICS↗
Multi-fidelity modeling to predict the rheological properties of fiber suspensions
Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications like biofuel production. The 3D numerical simulations of the suspension of fibers are often computationally expensive and time-consuming. Machine learning methods such as neural networks can simplify the prediction of rheological behavior; however, they require a relatively large training data set. Multi -fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified physical equations, can lead to optimized predictions. Here, we focus on a neural network with two levels of fidelity, i.e., high and low fidelity networks. To produce high-fidelity data, we perform direct numerical simulations to model the fibers as one-dimensional inextensible slender bodies that obey the Euler- Bernoulli beam equation. The Navier-Stokes equations govern the suspended fluid, and an immersed boundary method is used to couple the fluid and solid motion. The low-fidelity data is produced by using constitutive equations. Noticeable improvements have been observed in the accuracy of predicting the rheological behavior when a multi-fidelity network is used compared to the single-fidelity network.
Boodaghidizaji, Miad↗
Rethinking Materials Simulations Through Neural Operators
Invited talk at Machine Learning in Chemical and Materials Sciences 2025 [MLCM-25]
Dingreville, Remi Philippe Michel [Sandia National↗
Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks
Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact-detection algorithms, this process simultaneously returns several classes of false positives: false detections from poor-quality image subtractions, false detections from low signal-to-noise images, and detections of preexisting variable sources. Currently, human visual inspection to remove the false positives is a central part of multimessenger follow-up observations, but when next generation gravitational wave and neutrino detectors come online and increase the rate of multimessenger events, the visual inspection process will be prohibitively expensive. We approach this problem with two convolutional neural networks operating on the difference imaging outputs. The first network focuses on removing false detections and demonstrates an accuracy of 92% on our data set. The second network focuses on sorting all real detections by the probability of being a transient source within a host galaxy and distinguishes between various classes of images that previously required additional human inspection. We find the number of images requiring human inspection will decrease by a factor of 1.5 using our approach alone and a factor of 3.6 using our approach in combination with existing algorithms, facilitating rapid multimessenger counterpart identification by the astronomical community.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗