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DOE OSTI2024

Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting

Many existing object counting methods rely on density map estimation (DME) of the discrete grid representation by decoding extracted image semantic features from designed convolutional neural networks (CNNs). Relying on discrete density maps not only leads to information loss dependent on the original image resolution, but also has a scalability issue when analyzing high-resolution images with cubically increasing memory complexity. Furthermore, none of the existing methods can offer reliable uncertainty quantification (UQ) for the derived count estimates. To overcome these limitations, we design UNcertainty-aware, hypernetwork-based Implicit neural representations for Counting (UNIC) to assign probabilities and the corresponding counting confidence over continuous spatial coordinates. We derive a sampling-based Bayesian counting loss function and develop the corresponding model training algorithm. UNIC outperforms existing methods on the Remote Sensing Object Counting (RSOC) dataset with reliable UQ and improved interpretability of the derived count estimates. Our code is available at https://github.com/SiyuanXu-tamu/UNIC.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Accurate data-driven surrogates of dynamical systems for forward propagation of uncertainty

Stochastic collocation (SC) is a well-known non-intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for full-field uncertainty propagation that characterizes the distributions of the high-dimensional solution fields of a model with stochastic input parameters. However, due to the highly nonlinear nature of the parameter-to-solution map in even the simplest dynamical systems, the constructed SC surrogates are often inaccurate. Here, this work presents an alternative approach, where we apply the SC approximation over the dynamics of the model, rather than the solution. By combining the data-driven sparse identification of nonlinear dynamics framework with SC, we construct dynamics surrogates and integrate them through time to construct the surrogate solutions. We demonstrate that the SC-over-dynamics framework leads to smaller errors, both in terms of the approximated system trajectories as well as the model state distributions, when compared against full-field SC applied to the solutions directly. We present numerical evidence of this improvement using three test problems: a chaotic ordinary differential equation, and two partial differential equations from solid mechanics.

42 ENGINEERING↗

DOE OSTI2023

Thermofield Theory for Finite-Temperature Electronic Structure

Wave function methods have offered a robust, systematically improvable means to study ground-state properties in quantum many-body systems. Theories like coupled cluster and their derivatives provide highly accurate approximations to the energy landscape at a reasonable computational cost. Analogues of such methods to study thermal properties, though highly desirable, have been lacking because evaluating thermal properties involve a trace over the entire Hilbert space, which is a formidable task. Besides, excited-state theories are generally not as well studied as ground-state ones. In this mini-review, we present an overview of a finite-temperature wave function formalism based on thermofield dynamics to overcome these difficulties. Thermofield dynamics allows us to map the equilibrium thermal density matrix to a pure state, i.e., a single wave function, albeit in an expanded Hilbert space. Ensemble averages become expectation values over this so-called thermal state. Around this thermal state, we have developed a procedure to generalize ground-state wave function theories to finite temperatures. As explicit examples, we highlight formulations of mean-field, configuration interaction, and coupled cluster theories for thermal properties of Fermions in the grand-canonical ensemble. To assess the quality of these approximations, we also show benchmark studies for the one-dimensional Hubbard model, while comparing against exact results. We will see that the thermal methods perform similarly to their ground-state counterparts, while merely adding a prefactor to the asymptotic computational cost. Furthermore, they also inherit all the properties, good or bad, from the ground-state methods, signifying the robustness of our formalism and the scope for future development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Spring Dust in Colorado Plateau: Transport Pathways and Interannual Variability Derived From Two Decades of MERRA‐2 Reanalysis

Spring dust storms in the U.S. Southwest significantly impact environmental and human systems, yet their climatological patterns and driving mechanisms remain poorly understood. Using two decades of MERRA-2 reanalysis data and self-organizing map clustering, we identified four distinct dust transport pathways from surrounding and remote deserts for the top 10% of spring dust events impacting the Colorado Plateau: (a) intense southwesterly near-surface transport where eastward upward transport is blocked by topography, (b) substantial remote transport plus weaker surface emissions, (c) moderate southwesterly transport with stronger upper-level transport, and (d) rare northwesterly pattern linked to the Great Basin sources, which accounts for 38%, 14%, 37%, and 11% of total events, respectively. The frequency and intensity of these events strongly correlate with large-scale climate variability, with increased dust events during La Niña. Our results provide a robust framework for improving seasonal dust forecasts and understanding future dust dynamics under climate change.

aerosol transport↗

DOE OSTI2021

Risk-Averse Bi-Level Stochastic Network Interdiction Model for Cyber-Security Risk Management

Security of cyber networks is crucial; recent severe cyber-attacks have had a devastating effect on many large organizations. The attack graph, which maps the potential attack paths of a cyber network, is a popular tool for analyzing cyber system vulnerability. In this study, we propose a bi-level stochastic network interdiction model on an attack graph to enable a risk-averse, resource constrained cyber network defender to optimally deploy security countermeasures that protect against attackers with an uncertain budget. This risk- averse conditional-value-at-risk (CVaR) model minimizes a weighted sum of the expected maximum loss over all scenarios and the expected maximum loss from the most damaging attack scenarios. We develop a customized constraint and column generation algorithm to solve our model as well as several acceleration techniques to improve the computational efficiency. Numerical experiments demonstrate that the acceleration techniques enable the solution of relatively large problems within a reasonable amount of time: applying all the acceleration techniques also reduces the average computation time of the basic algorithm by 71% for 100-node graphs. Using metrics called mean-risk value of stochastic solution and value of risk-aversion, computational results suggest that our stochastic risk-averse model significantly outperforms deterministic and risk-neutral models when 1) the distribution of attacker budget is heavy-right-tailed and 2) the defender is highly risk-averse.

Bhuiyan, Tanveer H.↗

DOE OSTI2023

Multimodal and multiscale strengthening mechanisms in Al-Ni-Zr-Ti-Mn alloy processed by laser powder bed fusion additive manufacturing

The unique thermokinetics of laser-powder bed fusion additive manufacturing (L-PBFAM) has been exploited for development of a novel high-strength Al-Ni-Ti-Zr-Mn alloy. The addition of 0.5 wt% Mn leads to extraordinary improvement in ultimate tensile strength (502 MPa) and work hardening due to the activation of two Mn-induced strengthening mechanisms. First, by a bimodal particle strengthening effect due to Al 31 Mn 6 Ni 12 nano-quasi-crystal particles rejected in inter-dendritic spaces and fibrous Al 3 Ni eutectic dendritic channels, which predominately contributes to the strength improvement, and second by solid solution strengthening from remaining Mn entrapped in Al. These mechanisms supplement the particle strengthening effect imparted by coherent and incoherent Al 3 (Ti,Zr) co-precipitates present at melt pool boundaries, dislocation strengthening due to solidification induced strain, and Hall-Petch and backstress strengthening effect due to heterogenous grain size distribution occurring at various length scales. In conclusion, the solidification dynamics and hierarchical heat distribution that are associated with L-PBFAM resulted in complex spatial variations in these strengthening phenomena and were investigated via a high-throughput multiscale structure–property correlation that involved thermokinetic simulation, X-ray diffraction, high-resolution nanoindentation mapping, and site-specific transmission electron microscopy of the alloy.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Computational insights into the interaction of water with the UiO-66 metal–organic framework and its functionalized derivatives

The UiO-66 metal–organic framework (MOF) has been identified as a promising hydrophilic material for water harvesting. Recent studies show that its water uptake ability at low relative humidity (RH) can be improved by incorporating hydrophilic functional groups into the framework. In this work, we provide computational insights into the adsorption of water in UiO-66 and its functionalized derivatives to reveal the role played by different adsorption sites and functional groups in the adsorption mechanism. We started by developing molecular models for UiO-66, UiO-66-NH 2 , UiO-66-OH, and UiO-66-(OH) 2 compatible with the MB-pol data-driven many-body potential of water. We then benchmarked these models against ab initio data. Here we used these models to perform molecular dynamics simulations and calculate radial distribution functions, IR spectra, and two-dimensional density distribution maps for water in the MOFs. These results consistently show that the μ 3 -OH sites are the preferential interaction sites for water in UiO-66 and all its variants, and the formation of localised water clusters inside the octahedral pores is responsible for the abrupt step in the experimental adsorption isotherms. Furthermore, the presence of functional groups in the framework allows water to cluster in the octahedral pores at lower RH, thus making the MOF a more efficient water harvester. Overall, this study provides molecular-level insights into the pore filling process of UiO-66 and its functionalized derivatives, which are needed for the design of efficient water harvesting materials based on MOFs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Identification of Final-State Neutrons in ANNIE

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton gadolinium-loaded (Gd-loaded) water Cherenkov detector located on the Booster Neutrino beamline at Fermilab. ANNIE's primary physics objectives include measuring neutron multiplicity for neutrino-nucleus interactions and performing cross-section measurements of charge current quasi-elastic and neutral current quasi-elastic processes. These measurements aim to improve neutrino energy reconstruction and reduce uncertainties in current and future neutrino oscillation experiments. Additionally, ANNIE serves as a testbed for advanced technologies such as Large Area Picosecond Photodetectors (LAPPDs) and Water-Based Liquid Scintillator, which enhance vertex resolution and enable detection below the Cherenkov threshold. By leveraging the high neutron capture cross section of Gd-loaded water, ANNIE is well positioned to observe final-state neutrons in beam-correlated neutrino interactions. To constrain the uncertainties in the neutron capture efficiency and time within the detector, we have conducted multiple calibration campaigns using an AmBe source. The AmBe source is deployed at different locations of the tank to map the neutron capture efficiency and determine the expected neutron capture time. Additionally, the AmBe calibration data are used to define the precise neutron clustering that will be useful for identifying neutron-like clusters from neutrino interaction. In this poster, we will present the current status of the AmBe neutron calibration program and the development of neutron cluster definition for the ANNIE Experiment.

Ajana, Dhavalkumar [Florida State U.]↗

DOE OSTI2026

Emerging anomaly detection techniques for electronic health records: A survey

Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust. Methods This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems. Results We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare. Summary The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other domains or recently applied in the EHR domain are also discussed in detail. Although DL-based approaches can improve model predictions by incorporating comorbidities, their application is limited in low-frequency data domains (e.g., when the total available data remains in the single digits). Therefore, the user must carefully consider the application based on data availability.

Anomaly detection↗

DOE OSTI2022

Diagnosing the pedestal magnetic field and magnetohydrodynamics radial structure with pedestal–scrape of layer electron cyclotron emission radiation inversion in H-mode plasma (invited)

Forward modeling is used to interpret inversion patterns of the pedestal–Scrape of Layer (SOL) Electron Cyclotron Emission (ECE) in DIII-D H-mode experiments. The modeling not only significantly improves the ECE data interpretation quality but also leads to the potential measurements of (1) the magnetic field strength |B| at the separatrix, (2) the pedestal |B| evolution during an inter-Edge Localized Mode (ELM) period, and (3) the pedestal Magnetohydrodynamics (MHD) radial structure. The ECE shine-through effect leads to three types of pedestal–SOL radiation inversions that are discussed in this paper. The first type of inversion is the non-monotonic T e,rad profile with respect to the major radius. Using the ECE frequency at the minimum T e,rad , the inversion can be applied to measure the magnetic field |B| at the separatrix and calibrate the mapping of the ECE channels with respect to the separatrix. The second type of inversion refers to the opposite phase between the radiation fluctuations δT e,rad at the pedestal and SOL. This δT e,rad phase inversion is sensitive to density and temperature fluctuations at the pedestal foot and, thus, can be used to qualitatively measure the MHD radial structure. The third type of inversion appears when the pedestal and SOL T e,rad evolve in an opposite trend, which can be used to infer the pedestal |B| field change during an inter-ELM period. Finally, the bandwidth effect on measuring δT e,rad due to pedestal MHD is also investigated in the radiation modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

effect of ambient pressure on the behavior of single-component fuels in a gasoline multi-hole injector

The injection characteristics of neat ethanol and pure isooctane are studied under different ambient pressure and temperature conditions. Injection underflash-boiling conditions can enhance liquid atomization and evaporation, providing the possibility of improvement in the fuel/air mixing. These superheated conditions often introduce phenomena that are not taken into account in the standard modeling of sprays for engine applications. The present work proposes a numerical investigation of the behavior of Engine Combustion Network's 8-hole spray-G injector, starting at the subcooled nominal condition and reducing the ambient pressure at constant low temperature to reach the flare flash-boiling condition. To initialize the properties of the injected fuel, the flow in the nozzle is simulated with a Eulerian approach, handling the two phases with a mixture model and the phase change, due to cavitation and flash boiling, with the Homogenous Relaxation Model. A map of the mixture's kinematic and thermal behavior is obtained at the interface between the injector and the chamber to initialize the Lagrangian simulations. A literature-based vaporization model is implemented to obtain the proper description of the characteristic features of a multi-hole spray under super-heated conditions, like plume-plume interaction. The numerical representation of the spray is validated in terms of penetration and radial spreading on DBI images, reproducing the light attenuation profiles caused by the presence of the liquid spray. Simulations show that coupled nozzle flow and spray calculations capture the spray morphology and shape better compared to calculations performed without considering the nozzle flow simulation details, especially under flare flash conditions.

Nocivelli, Lorenzo↗

DOE OSTI2025

ChemComp: A Compilation Framework for Computing with Chemical Reaction Networks

The acceleration of scientific computation, data analytics, and artificial intelligence is driving a surge in computational requirements. Yet, state-of-the-art high-performance computing systems are approaching physical limitations that impede further significant improvements in energy efficiency. As we move towards post-exascale computing systems, innovative approaches are necessary to overcome this barrier in power consumption. Novel analog and hybrid digital-analog architectures hold promise for enhancing energy efficiency by several orders of magnitude. Biochemical computation stands out among the various solutions being explored due to its potential to enable new classes of devices with immense computational capabilities. These devices can capitalize on the inherent efficacy of biological cells in solving optimization problems and are scalable through increasing reaction system size or vessel capacity, potentially satisfying scientific computing's high-performance requirements. Nonetheless, several theoretical and practical limitations persist, including problem formulation and mapping to chemical reaction networks (CRNs) and implementation of actual CRN devices. In this paper, we propose a framework for biochemical computation using systems chemistry. We present the initial components of our approach: an abstract chemical reaction dialect implemented as a multi-level intermediate representation (MLIR) compiler extension and a pathway to represent mathematical problems with CRNs. To showcase the potential of this approach, we emulate a simplified chemical reservoir device. This work lays the groundwork for leveraging chemistry's computing potential in creating energy-efficient, high-performance computing systems tailored to contemporary computational needs.

artificial intelligence↗

DOE OSTI2022

A Universal Refrigerant Charge Fault Detection and Diagnostics Method Based on Pump Down Operation

The performance of the heat pump system varies greatly depending on the refrigerant charge amount. Improving the refrigerant charge fault detection and diagnostics (FDD) method of vapor compression systems have the potential for increasing energy efficiency and reducing service cost. Previous studies to predict refrigerant charge amount are mostly empirical methods which require significant amount of experimental data for high accuracy. The primary goal of this research is to develop a universal charge fault detection method which requires only a few experimental data with high prediction accuracy.Currently, pump down operations are typical practices by HVAC technicians when they need to open the refrigerant circuit to make a repairment. In addition, compressors have a built-in low-pressure cut-off protection function, and the compressor performance maps are commonly available from manufacturers. The proposed method innovatively utilizes the typical pump down operation, the compressor low-pressure cut-off protection, and the compressor performance map. It does not require any geometry information of heat exchangers, refrigerant lines, or charge buffers.The new charge prediction method is firstly formulated through theoretical analysis, then verified and calibrated by a quasi-steady-state simulation of the pump down process for a residential heat pump system. The quasi steady-state simulation uses an HVAC system simulation framework driven by DOE/ORNL Heat Pump Design Model (HPDM). Preliminary experiment validations with heat pump refrigerant leakage tests demonstrate the deviation of the proposed charge prediction method compared with measurement is within 8%. This technology makes refrigerant charge amount available at the technician’s fingertips and leads to shorter maintenance time and fewer site visits.

Li, Zhenning↗

DOE OSTI2022

A Comprehensive Understanding of the Aging Effects of Extreme Fast Charging on High Ni NMC Cathode

As the battery industry shifts toward high Ni content cathodes, such as LiNi 0.8 Mn 0.1 Co 0.1 O 2 [NMC811], a complete understanding of the degradation mechanisms of NMC811 under extreme fast charging (XFC) (XFC, ≤10–15 min charging) conditions is needed. Such comprehensive understanding would identify the most critical materials gaps that need to be addressed for enabling XFC long-life cells for electric vehicles. This study maps out the key aging mechanisms for NMC811 cycled at different XFC conditions (between 1C and 9C) for up to 1000 cycles. To acquire a fundamental understanding of utilization and degradation, cells are evaluated using a range of electrochemical techniques, and multimodal and multiscale microscopy techniques to quantify chemical, structural, and crystallographic degradation as a function of cycling conditions for the NMC cathode. When comparing NMC811 to NMC532, it is observed that NMC811 has a greater subsurface crystallographic degradation and displays a similar magnitude of subparticle cracking. However, the NMC811 maintains superior performance despite those advanced degradations. Here, the superior cycle life performance is attributed to the NMC811 particles having radially oriented grains and improved transport properties. NMC811 shows between 4.6× and 3.15× reduction in capacity fade than NMC532 for charging rates between 4C (e.g., 15-min charging) and 6C (10-min charging).

25 ENERGY STORAGE↗

DOE OSTI2023

Uniform-in-phase-space data selection with iterative normalizing flows

Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that are routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas dimension reduction typically aims at describing each data sample on lower-dimensional space, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data are available.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Future Physics Perspectives on the Equation of State from Heavy Ion Collisions to Neutron Stars

With the computational power and algorithmic improvements available today, the ongoing STAR/RHIC and HADES/GSI experiments, the future FAIR and NICA facilities becoming operational, and the new precise measurements from NICER and LIGO/VIRGO, the high-energy nuclear physics and astrophysics communities are in the unique position to set very stringent constraints on the equation of state of strongly interacting matter. Here, we review the state-of-the-art of different approaches used in the description of hot and ultradense baryonic matter in and out of equilibrium, and discuss the regions in the phase diagram where heavy-ion collisions and neutron star mergers can overlap. Future perspectives are discussed to help define a comprehensive, multi-disciplinary strategy to map out the phase diagram of strongly interacting matter from heavy ion collisions to neutron stars.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Optimizing the shape of photometric redshift distributions with clustering cross-correlations

We present an optimization method for the assignment of photometric galaxies to a chosen set of redshift bins. This is achieved by combining simulated annealing, an optimization algorithm inspired by solid-state physics, with an unsupervised machine learning method, a self-organizing map (SOM) of the observed colours of galaxies. Starting with a sample of galaxies that is divided into redshift bins based on a photometric redshift point estimate, the simulated annealing algorithm repeatedly reassigns SOM-selected subsamples of galaxies, which are close in colour, to alternative redshift bins. We optimize the clustering cross-correlation signal between photometric galaxies and a reference sample of galaxies with well-calibrated redshifts. Depending on the effect on the clustering signal, the reassignment is either accepted or rejected. By dynamically increasing the resolution of the SOM, the algorithm eventually converges to a solution that minimizes the number of mismatched galaxies in each tomographic redshift bin and thus improves the compactness of their corresponding redshift distribution. This method is demonstrated on the synthetic Legacy Survey of Space and Time cosmoDC2 catalogue. We find a significant decrease in the fraction of catastrophic outliers in the redshift distribution in all tomographic bins, most notably in the highest redshift bin with a decrease in the outlier fraction from 57 percent to 16 percent.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Selective Sampling for Sensor Type Classification in Buildings

A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata --- a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. This framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11,000 sensors of 18 different types. The experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗