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DOE OSTI2025

DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

FAIR Surrogate Benchmarks Supporting AI and Simulation Research (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia (UVA). SBI repositories include data, code, and all relevant collateral artifacts that the science and engineering community need to use and reuse these data sets and surrogates. SBI repositories generate active research from both the participants in SBI and the broad community of AI and domain scientists. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and captures them as surrogate benchmarks with a rich set of metadata covering: Data; Model; Metrics specification; Machine specification; and Science, Speed, and Power Results. We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non-Surrogate benchmarks that have many common features and similar issues as regards FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, Benchmarks have datasets, models, and metadata and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Machine Learning in Infectious Disease for Risk Factor Identification and Hypothesis Generation: Proof of Concept Using Invasive Candidiasis

Machine learning (ML) models can handle large data sets without assuming underlying relationships and can be useful for evaluating disease characteristics, yet they are more commonly used for predicting individual disease risk than for identifying factors at the population level. We offer a proof of concept applying random forest (RF) algorithms to Candida-positive hospital encounters in an electronic health record database of patients in the United States. Candida-positive encounters were extracted from the Cerner HealthFacts database; invasive infections were laboratory-positive sterile site Candida infections. Features included demographics, admission source, care setting, physician specialty, diagnostic and procedure codes, and medications received before the first positive Candida culture. We used RF to assess risk factors for 3 outcomes: any invasive candidiasis (IC) vs non-IC, within-species IC vs non-IC (eg, invasive C. glabrata vs noninvasive C. glabrata), and between-species IC (eg, invasive C. glabrata vs all other IC). Fourteen of 169 (8%) variables were consistently identified as important features in the ML models. When evaluating within-species IC, for example, invasive C. glabrata vs non-invasive C. glabrata, we identified known features like central venous catheters, intensive care unit stay, and gastrointestinal operations. In contrast, important variables for invasive C. glabrata vs all other IC included renal disease and medications like diabetes therapeutics, cholesterol medications, and antiarrhythmics. Known and novel risk factors for IC were identified using ML, demonstrating the hypothesis-generating utility of this approach for infectious disease conditions about which less is known, specifically at the species level or for rarer diseases.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Evaluating generative networks using Gaussian mixtures of image features

We develop a measure for evaluating the performance of generative networks given two sets of images. A popular performance measure currently used to do this is the Fréchet Inception Distance (FID). However, FID assumes that images featurized using the penultimate layer of Inception follow a Gaussian distribution. This assumption allows FID to be easily computed, since FID uses the 2-Wasserstein distance of two Gaussian distributions fitted to the featurized images. However, we show that Inception features of the ImageNet dataset are not Gaussian; in particular, each marginal is not Gaussian. To remedy this problem, we model the featurized images using Gaussian mixture models (GMMs) and compute the 2-Wasserstein distance restricted to GMMs. We define a performance measure, which we call WaM, on two sets of images by using inception (or another classifier) to featurize the images, estimate two GMMs, and use the restricted 2-Wasserstein distance to compare the GMMs. We experimentally show the advantages of WaM over FID, including how FID is more sensitive than WaM to image perturbations. By modelling the non-Gaussian features obtained from inception as GMMs and using a GMM metric, we can more accurately evaluate generative network performance.

machine learning, genrative adversarial networks↗

DOE OSTI2022

Load Disaggregation (Modeling Individual Appliance Power Generation & Consumption in Real-Time)

We developed a machine learning-based load disaggregation method to estimate the real-time output of individual appliances from the whole-house measurements. We first learn the important features associated with each type of appliances using the ground truth consumption data of individual appliances potentially available for a small set of houses equipped with submeters. The learned features are then be used to estimate the power generation/consumption of the appliances from the whole-house consumption. This developed load disaggregation software includes two steps. The first step is to identify the on/off status of different appliances using a classification method, and the second step is to estimate the appliance output using a regression method.

Hao, Jun↗

DOE OSTI2022

HyMARC Seedling: Optimized Hydrogen Adsorbents via Machine Learning and Crystal Engineering

This final report is presented in two parts: In Part 1, the H 2 capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials. The identified MOFs are predominantly hypothetical compounds having low densities (<0.31 g/cm3) in combination with high surface areas (>5,300 m2/g), void fractions ($0.90), and pore volumes (>3.3 cm3/g). The relative importance of the input features are characterized, and dependencies on the ML algorithm and training set size are quantified. The most important features for predicting H 2 uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available on the web, allowing for rapid and accurate predictions of the hydrogen capacities of MOFs from limited structural data; the simplest models require only a single crystallographic feature. In part 2, ways to improve the poor powder packing density of MOFs is discussed. More specifically, a strategy that improves packing efficiency and volumetric hydrogen gas storage density dramatically through engineered morphologies and controlled-crystal size distributions is presented that holds promise for maximizing storage capacity for a given MOF. The packing density improvement, demonstrated for the benchmark sorbent MOF-5, leads to a significant enhancement of volumetric hydrogen storage performance relative to commercial MOF-5. System model projections demonstrate that engineering of crystal morphology/size or use of a bimodal distribution of cubic crystal sizes in tandem with system optimization can surpass the 25 g/L volumetric capacity of a typical 700 bar compressed storage system and exceed the DOE targets 2020 volumetric capacity (30 g/L). Finally, a critical link between improved powder packing density and reduced damage upon compaction is revealed leading to sorbents with both high surface area and high density.

08 HYDROGEN↗

DOE OSTI2022

Predicting defects in laser powder bed fusion using in-situ thermal imaging data and machine learning

Variation in the local thermal history during the Laser Powder Bed Fusion (LPBF) process in Additive Manufacturing (AM) can cause micropore defects, which add to the uncertainty of the mechanical properties (e.g., fatigue life, tensile strength) of the built materials. In-situ sensing has been proposed for monitoring the AM process to minimize defects, but successful minimization requires establishing a quantitative relationship between the sensing data and the porosity, which is particularly challenging with a large number of variables (e.g., laser speed, power, scan path, powder property). Physics-based modeling can simulate such an in-situ sensing-porosity relationship, but it is computationally costly. In this work, we develop Machine Learning (ML) models that can use in-situ thermographic data to predict the micropore of LPBF stainless steel materials. This work considers two identified key features from the thermal histories: the time above the apparent melting threshold ($\tau$) and the maximum radiance ( T max ). These features are computed, stored for each voxel in the built material, and then used as inputs. The binary state of each voxel, either defective or normal, is the output. Different ML models are trained and tested for the binary classification task. In addition to using the thermal features of each voxel to predict its own state, the thermal features of neighboring voxels are also included as inputs. This is shown to improve the prediction accuracy, which is consistent with thermal transport physics around each voxel contributing to its final state. Among the models trained, the F1 scores on test sets reach above 0.96 for Random Forests. Feature importance analysis based on the ML models shows that T max is more important to the voxel state than $\tau$ . The analysis also finds that the thermal history of the voxels above the present voxel is more influential than those beneath it. Our study significantly extends the capability of using in-situ thermographic data to predict porosity in LPBF materials. Finally, since ML models are fast, they may play integral roles in the optimization and control of such AM technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Time-Dependent Electric Bus and Charging Station Deployment Problem

Battery electric buses (BEBs) have gained popularity due to their emission-free and energy-efficient features. Many transit authorities worldwide have set goals to gradually replace their bus fleets with BEBs. Considering the potential decline in BEB battery and charger prices, this study proposes a time-dependent bus fleet transition model to determine the optimal bus fleet transition plan, which includes selecting the bus lines to be electrified, determining the timing and type of BEBs to be purchased, and deploying on-route fast chargers and depot chargers. The model is a bi-objective integer linear program that considers the trade-off between electrified transit mileages and bus electrification costs. A normalized normal constraint method is applied to solve the bi-objective optimization model. The effectiveness of the proposed model is tested using a real-world bus network. Additionally, sensitivity analyses are conducted to better understand the impact of different parameter values on the optimal solutions. Our proposed model can provide transit authorities with a powerful tool to make informed decisions about their BEB fleet replacement plans.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

High-dimensional data analytics in civil engineering: A review on matrix and tensor decomposition

Recent developments in sensing and monitoring techniques have led to the generation of high-dimensional data in the field of civil engineering. High-dimensional data analytics methods have thus been developed to interpret such complex data. Among the different high-dimensional data analytics techniques, matrix and tensor decomposition methods have acquired a notable interest in the civil engineering community over the past decade. Due to their unique ability to deal with highly redundant and correlated data, these methods are establishing themselves as promising and efficient tools to analyze high-dimensional data in the civil engineering arena. In this paper, high-dimensional data is referred to as a data set in which the number of features is comparable or larger than the number of observations. This review paper aims to summarize the applications of matrix and tensor decomposition methods in civil engineering over the last decade. The survey begins with a general overview of matrix and tensor decomposition followed by highlighting their significance in the field. Afterward, various applications of these high-dimensional data analytics methods in civil engineering are presented, while the advantages offered by these methods are discussed. Lastly, challenges and potential research avenues for employing matrix and tensor decomposition and future emerging trends for their novel use are highlighted.

42 ENGINEERING↗

DOE OSTI2025

Overcoming fundamental challenges of the electrochemical nitrogen oxidation reaction

Nitric acid and nitrates play a crucial role in modern agriculture and medicine; however, their commercial production through the Haber–Bosch and Ostwald processes is energy-intensive and generates significant greenhouse gas emissions. Additionally, the rapidly growing global population is expected to significantly increase food demand in the coming decades. Therefore, there is a pressing need to explore sustainable alternatives for synthetic fertilizer production to meet increasing global crop demands while reducing the carbon footprint. The electrochemical nitrogen oxidation reaction (NOR) presents a viable alternative which produces nitrates using renewable electricity. However, NOR is still a nascent reaction that has not been studied extensively, and several key challenges must be addressed as the field develops. In this feature article review, we highlight the set of effective approaches that we have employed for studying this complex reaction and overcoming these challenges, as well as discuss future directions for advancing NOR technology.

Energy - Conversion↗

DOE OSTI2025

Fast jet tagging with MLP-Mixers on FPGAs

We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. MLP-Mixers excel in processing sequences of jet constituents, achieving state-of-the-art performance on datasets mimicking Large Hadron Collider conditions. By using advanced optimization techniques such as High-Granularity Quantization and Distributed Arithmetic, we achieve unprecedented efficiency. These models match or surpass the accuracy of previous architectures, reduce hardware resource usage by up to 97%, double the throughput, and half the latency. Additionally, non-permutation-invariant architectures enable smart feature prioritization and efficient FPGA deployment, setting a new benchmark for machine learning in real-time data processing at particle colliders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Return of the templates: Revisiting the Galactic Center excess with multimessenger observations

The Galactic center excess (GCE) remains one of the most intriguing discoveries from the Fermi Large Area Telescope (LAT) observations. We revisit the characteristics of the GCE by first producing a new set of high-resolution galactic diffuse gamma-ray emission templates. This diffuse emission, which accounts for the bulk of the observed gamma rays, is ultimately due to cosmic-ray interactions with the interstellar medium. Using recent high-precision cosmic-ray observations, in addition to the continuing Fermi-LAT observations and observations from lower energy photons, we constrain the properties of the galactic diffuse emission. We describe a large set of diffuse gamma-ray emission templates which account for a very wide range of initial assumptions on the physical conditions in the inner galaxy. The broad properties of the GCE that we find in this work are qualitatively unchanged despite the introduction of this new set of templates, though its quantitative features appear mildly different than those obtained in previous analyses. In particular, we find a high-energy tail at higher significance than previously reported. This tail is very prominent in the northern hemisphere, and less so in the southern hemisphere. This strongly affects one prominent interpretation of the excess: known millisecond pulsars are incapable of producing this high-energy emission, even in the relatively softer southern hemisphere, and are therefore disfavored as the sole explanation of the GCE. The annihilation of dark matter particles of mass 40 - 7 + 10 GeV (95% CL) to b quarks with a cross-section of ⟨ σ A v ⟩ = 1. 4 - 0.3 + 0.6 × 10 - 26 cm 3 s − 1 provides a good fit to the excess especially in the relatively cleaner southern sky. Dark matter of the same mass range annihilating to b quarks or heavier dark matter particles annihilating to heavier Standard Model bosons can combine with millisecond pulsars to provide a good fit to the southern hemisphere emission as well, as can a broken power-law spectrum which would be related to recent cosmic-ray burst activity. As part of this paper, we make publicly available all of our templates and the data covariance matrix we have generated to account for systematic uncertainties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Toward UV models of kinetic mixing and portal matter. VI. A more complex dark matter sector?

Portal matter (PM), having both Standard Model (SM) and dark sector charges, can induce kinetic mixing between the 𝑈⁢(1) 𝐷 dark photon and the SM gauge fields at the 1-loop level offering an attractive mechanism by which light (≲1 GeV) thermal dark matter (DM) can interact with visible matter and obtain its observed relic density. In doing so, if the DM is fermionic, the CMB and other astrophysical observations inform us that it must be Majorana/pseudo-Dirac in nature to avoid velocity/temperature-independent 𝑠-wave annihilation to SM final states. How does this idea fit into a more UV-complete picture also including the SM interactions? There are some reasons to believe that at least a first step along this path may not lie too far away in energy due to the renormalization group equations running of the dark gauge coupling, which for a significant range of parameters, becomes nonperturbative at/before the ∼10’s of TeV energy range. This implies that 𝑈⁢(1) 𝐷 must become embedded in an asymptotically free, non-Abelian group, 𝐺 𝐷 , before this can occur. The breaking of this larger group then produces the masses for the PM and the additional gauge fields associated with 𝐺 𝐷 then can lead to new interactions between the SM and the dark sector. Following several bottom-up approaches, we have examined a set of distinctive and testable phenomenological features associated with this general setup, based upon a number of simplifying assumptions. Clearly, it behooves us to explore the impact of these specific assumptions on these predictions for the array of possible experimental tests of this class of models. In most past analyses it has been assumed that DM is a vectorlike, complex singlet under the group 𝐺 𝐷 . If this assumption is relaxed, the dark sector must be augmented by additional fermion(s) and the associated scalar fields needed to break the gauge symmetries while generating the needed Majorana-like mass terms for the DM. In this paper, we analyze the simplest extension of this kind wherein the DM lies in a vectorlike doublet of 𝐺 𝐷 , which we take to have the structure 𝑆⁢𝑈⁢(2) 𝐼 ×𝑈⁢(1) 𝑌 𝐼 as in earlier work, leading to new phenomenological implications. We find, for example, that given the current LHC search constraints on the masses of heavy gauge bosons, the production of these new dark states with large rates is unlikely to occur at colliders unless they are produced singly in 𝑔⁡𝑔 fusion or their pair production cross sections are resonantly enhanced. Here, we also find that an additional mechanism arises to generate hierarchal neutrino masses in such a setup.

Extensions of Higgs sector↗

DOE OSTI2022

Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles

Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of en-semble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.

Han, Mengjiao↗

DOE OSTI2020

Toward a Theory of Superdense Time in Simulation Models

In this work, we develop a theory of superdense time that encompasses existing uses of superdense time in discrete event simulations and points to new forms that have not previously been explored. A central feature of our development is a set of axioms for superdense time. Furthermore, the sufficiency of these axioms is demonstrated by using them to prove that a general model of a discrete event simulation procedure, expressed in terms of a mathematical system, constitutes a state transition function. Several forms of superdense time, both known and novel, are shown to satisfy the axioms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

UMap: An application-oriented user level memory mapping library

Exploiting the prominent role of complex memories in exascale node architecture, the UMap page fault handler offers new capabilities to access large memory-mapped data sets directly. UMap provides flexible configuration options to customize page handling to each application, including analysis of massive observational and simulation data sets. The high-performance design features I/O decoupling, dynamic load balancing, and application-level controls. Page faults triggered by application threads and processes accessing data mapped to a UMapp’ed region are handled via the Linux userfaultfd protocol, an asynchronous message-oriented kernel-user communication mechanism that avoids the context switch penalty of traditional signal fault handlers. UMap is fully open source. In this paper, we give an overview of the UMap library architecture, its extensible plugin architecture, and the use/performance of UMap in emerging heterogeneous memory hierarchies such as near-node Non-volatile Memory (NVM) and network attached memories. We highlight new capabilities in two pagefault management plugins, the NetworkStore and SparseStore. We demonstrate the integration between UMap and multiple ECP products including Caliper, Metall, ZFP, Mochi, and Ripples.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Different tertiary interactions create the same important 3D features in a distinct flavivirus xrRNA

During infection by a flavivirus (FV), cells accumulate noncoding subgenomic flavivirus RNAs (sfRNAs) that interfere with several antiviral pathways. These sfRNAs are formed by structured RNA elements in the 3' untranslated region (UTR) of the viral genomic RNA, which block the progression of host cell exoribonucleases that have targeted the viral RNA. Previous work on these exoribonuclease-resistant RNAs (xrRNAs) from mosquito-borne FVs revealed a specific three-dimensional fold with a unique topology in which a ring-like structure protectively encircles the 5' end of the xrRNA. Conserved nucleotides make specific tertiary interactions that support this fold. Examination of more divergent FVs reveals differences in their 3' UTR sequences, raising the question of whether they contain xrRNAs and if so, how they fold. To answer this, we demonstrated the presence of an authentic xrRNA in the 3' UTR of the Tamana bat virus (TABV) and solved its structure by X-ray crystallography. The structure reveals conserved features from previously characterized xrRNAs, but in the TABV version these features are created through a novel set of tertiary interactions not previously seen in xrRNAs. This includes two important A–C interactions, four distinct backbone kinks, several ordered Mg 2+ ions, and a C + –G–C base triple. The discovery that the same overall architecture can be achieved by very different sequences and interactions in distantly related flaviviruses provides insight into the diversity of this type of RNA and will inform searches for undiscovered xrRNAs in viruses and beyond.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Verification and validation of developed short-term forecasting models

Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗