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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Destructive Analysis of TRISO Particles: Crush/Burn/Leach Followed by Davies-Gray Titration and IDMS

The accurate accounting of nuclear materials is a cornerstone of international nuclear safeguards. One emerging challenge in this domain is the fabrication of TRIstructural ISOtropic (TRISO) particle fuels. Although these innovative fuel forms are critical for advanced reactor applications, their robust refractory ceramics and coating compositions present significant obstacles to destructive analysis (DA) methods. Ensuring full and quantitative recovery from these particles is essential for accurate mass accountancy. The current study was initiated to address these challenges, first by validating a previously established destructive method developed by Oak Ridge National Laboratory (ORNL) for the quantitative recovery of uranium from TRISO particles and then following that process with uranium content determination through isotope dilution mass spectrometry (IDMS) and Davies-Gray titration. This study expands on the scope of a digestive method that was developed under the Advanced Gas Reactor Fuel Development and Qualification program and is currently implemented in both the Coated Particle Fuel Development Laboratory and Irradiated Fuels Examination Laboratory at ORNL. The success of the previous Advanced Gas Reactor work relied on developing a DA method to evaluate the fabrication process and reactor experiments. The methodology described in this report was designed to rigorously investigate the efficacy of the crush/burn/leach sample preparation of TRISO particles; it aims to quantify uranium recovery while also assessing the effects of TRISO constituents (e.g., silicon and zirconium) on analytical precision and accuracy. By comparing the results from the titration method and IDMS, we sought to determine whether existing analytical procedures accepted by the International Atomic Energy Agency (IAEA) could be effectively translated to TRISO fuel forms. The team employed an approach that involved processing replicate TRISO samples, optimizing the milling (i.e., crushing) step and performing serial leaches. The elemental composition of the analytical samples was examined to prepare for interference studies in the second year of this project. The integration of gamma spectrometry to verify residual uranium activity further strengthened the validation. Statistical methods were applied to the collected data to evaluate the uncertainties arising from sampling, sample preparation and uranium quantification. These uncertainties were then compared to the IAEA’s international target values (ITVs). Additional data collected in upcoming project work will strengthen the uncertainty estimates. Ultimately, it is hoped that this project will contribute materially to the body of work related to characterization of TRISO based fuels for the purpose of material accountancy and its applications to international safeguards.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Nuclear Structural Component Relevant Properties of Nickel-Based Alloys Produced via Additive Manufacturing

Idaho National Laboratory initiated examination of nickel-based alloys manufactured via three different additive manufacturing methods for potential applications in nuclear, high temperature structural components. The three methods analyzed included laser powder bed fusion, blown powder laser directed energy deposition, and wire-fed gas metal arc directed energy deposition. With the rapid push towards additive manufacturing, codes do not exist that definitively define what is or is not tolerable for each process and application, such as with conventional, wrought products. This report contains the initial work to understand possible manufacturing methods for high temperature alloys, and specifically, void formation, microstructure evolution, corrosion, and mechanical properties. To generate mechanical test data, specimens were tested irrespective of voids and microstructures were analyzed to better understand how to negate/improve these issues. The preliminary results showed major decreases in mechanical performance for material tested. Test specimens will continue to be produced to further improve each additive manufacturing processes, quantify void acceptance, and better understand the most suitable high temperature alloys receptive to additive manufacturing and high temperature nuclear applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Initial data for first-order causal viscous conformal fluids in general relativity

We solve the Einstein constraint equations for a first-order causal viscous relativistic hydrodynamic theory in the case of a conformal fluid. For such a theory, a direct application of the conformal method does not lead to a decoupling of the equations, even for constant-mean curvature initial data. We combine the conformal method applied to a background perfect fluid theory with a perturbative argument in order to obtain the result.

Disconzi, Marcelo (ORCID:0000000234497778)↗
DOE OSTI2025

Drying of strawberries with airborne ultrasound and other integrated dehydration mechanisms

This article presents a comprehensive investigation into the drying of strawberries using multiple dehydration methods with an emphasis on energy efficiency and sustainability. Initially, an airborne ultrasonic transducer with a frequency of 21 kHz was employed in batch operations to examine the effects of controlling parameters, including applied power, distance from the transducer plate, sample thickness, and sample holder type. The Energy Ratio, defined as the ratio of thermal energy required to evaporate moisture to ultrasonic energy, was observed to reach up to 2.2, particularly during the initial stages of drying. Subsequently, the Smart Dryer integrated airborne ultrasound (US) dehydration, slot jet reattachment (SJR) nozzle convective drying, infrared (IR) drying, and their combinations, enabling a systematic exploration of various drying conditions on the drying time and quality of strawberries. The integration of airborne US with SJR nozzles and IR drying demonstrates a promising approach for optimizing drying processes. This method not only preserves the quality of dried strawberries but also improves the energy factor to 0.88, reducing drying time by 89% compared with other conditions. Key quality attributes of the dried strawberries, such as color and water activity, were evaluated to understand the influence of each drying method. The findings highlight the potential of these integrated drying techniques as sustainable solutions for efficient and high-quality strawberry dehydration.

42 ENGINEERING↗
DOE OSTI2025

Using Neural Networks to Identify Mixture Components in Hyperspectral Reflectance Data

Neural networks have been employed to identify materials of interest from hyperspectral data (generally imagery) based on their unique spectral signatures. This approach assumes that there is a single material that is standing out from the rest of the spectrum to be identified. However, pixels often contain more than one material, or a material of interest may itself be a mixture of multiple materials. Neural networks are only as good as the data used to train them, and it takes a great deal of work in the laboratory to identify, make, and measure all potential mixtures of interest. Thus, researchers often calculate synthetic spectra using algorithms with varying degrees of fidelity to the physics that govern the interactions between light and multiple materials. In this work, we have (1) adapted a neural network designed to identify mixture components from Raman spectroscopy to work with visible to near‐infrared reflectance data and (2) tested three common mixture algorithms to determine the most accurate and least computationally expensive method to build synthetic training datasets. With our initial test dataset, we have achieved accuracies of > 90% and found that the synthetic training dataset produced using the Hapke mixture model provides the best results.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Prediction of α $IIb$ $β$ 3 integrin structures along its minimum free energy activation pathway

The adhesion protein integrin is a transmembrane heterodimer that plays a pivotal role in cellular processes such as cell signaling and cell migration. To execute its function, integrin undergoes extensive conformational changes from a bent-closed to an extended-open state. Resolving the structures across these changes remains a challenge with both experimental and computational methods, but it is crucial for understanding the activation mechanism of integrin. We address this challenge for the platelet integrin α IIb β 3 by employing finite temperature string method with structures of the images along the initial guess path generated by a multiscale data-driven framework. The full-length all-atom structures along the resulting minimum free energy path between the inactive bent-closed and active extended-open states of α IIb β 3 integrin are consistent with a variety of experimentally resolved structures. Changes in these predicted structures along the path show that the extension and separation of the α and β subunits from the bent-closed to the extended-open state require correlated movements between the subdomain pairs in α IIb β 3 . Furthermore, these results provide new insights into integrin activation mechanism, and the predicted structures have potential applications in guiding the design of integrin-targeting therapeutics.

Dasetty, Siva [University of Chicago, IL (United S↗
DOE OSTI2024

Rapidly convergent quantum Monte Carlo using a Chebyshev projector

The multireference coupled-cluster Monte Carlo (MR-CCMC) algorithm is a determinant-based quantum Monte Carlo (QMC) algorithm that is conceptually similar to Full Configuration Interaction QMC (FCIQMC). It has been shown to offer a balanced treatment of both static and dynamic correlation while retaining polynomial scaling, although application to large systems with significant strong correlation remained impractical. In this paper, we document recent algorithmic advances that enable rapid convergence and a more black-box approach to the multireference problem. These include a logarithmically scaling metric-tree-based excitation acceptance algorithm to search for determinants connected to the reference space at the desired excitation level and a symmetry-screening procedure for the reference space. We show that, for moderately sized reference spaces, the new search algorithm brings about an approximately 8-fold acceleration of one MR-CCMC iteration, while the symmetry screening procedure reduces the number of active reference space determinants with essentially no loss of accuracy. We also introduce a stochastic implementation of an approximate wall projector, which is the infinite imaginary time limit of the exponential projector, using a truncated expansion of the wall function in Chebyshev polynomials. Notably, this wall-Chebyshev projector can be used to accelerate any projector-based QMC algorithm. We show that it requires significantly fewer applications of the Hamiltonian to achieve the same statistical convergence. We benchmark these acceleration methods on the beryllium and carbon dimers, using initiator FCIQMC and MR-CCMC with basis sets up to cc-pVQZ quality.

Zhao, Zijun↗
DOE OSTI2024

Nine lensed quasars and quasar pairs discovered through spatially extended variability in Pan-STARRS

We present the proof of concept of a method for finding strongly lensed quasars using their spatially extended photometric variability through difference imaging in cadenced imaging survey data. We applied the method to Pan-STARRS, starting with an initial selection of 14 107Gaiamultiplets with quasar-like infrared colours from WISE. We identified 229 candidates showing notable spatially extended variability during the Pan-STARRS survey period. These include 20 known lenses and an additional 12 promising candidates for which we obtained long-slit spectroscopy follow-up. This process resulted in the confirmation of four doubly lensed quasars, four unclassified quasar pairs, and one projected quasar pair. Only three are pairs of stars or quasar+star projections. The false-positive rate accordingly is 25%. The lens separations are between 0.81″ and 1.24″, and the source redshifts lie betweenz = 1.47 andz = 2.46. Three of the unclassified quasar pairs are promising dual-quasar candidates with separations ranging from 6.6 to 9.3 kpc. We expect that this technique is a particularly efficient way to select lensed variables in the upcomingRubin-LSST, which will be crucial given the expected limitations for spectroscopic follow-up.

Astronomy & Astrophysics↗
DOE OSTI2024

Double diffraction imaging of x-ray induced structural dynamics in single free nanoparticles

Abstract Because of their high photon flux, x-ray free-electron lasers (FEL) allow to resolve the structure of individual nanoparticles via coherent diffractive imaging (CDI) within a single x-ray pulse. Since the inevitable rapid destruction of the sample limits the achievable resolution, a thorough understanding of the spatiotemporal evolution of matter on the nanoscale following the irradiation is crucial. We present a technique to track x-ray induced structural changes in time and space by recording two consecutive diffraction patterns of the same single, free-flying nanoparticle, acquired separately on two large-area detectors opposite to each other, thus examining both the initial and evolved particle structure. We demonstrate the method at the extreme ultraviolet (XUV) and soft x-ray Free-electron LASer in Hamburg (FLASH), investigating xenon clusters as model systems. By splitting a single XUV pulse, two diffraction patterns from the same particle can be obtained. For focus intensities of about 2 × 10 12 W cm −2 we observe still largely intact clusters even at the longest delays of up to 650 picoseconds of the second pulse, indicating that in the highly absorbing systems the damage remains confined to one side of the cluster. Instead, in case of five times higher flux, the diffraction patterns show clear signatures of disintegration, namely increased diameters and density fluctuations in the fragmenting clusters. Future improvements to the accessible range of dynamics and time resolution of the approach are discussed.

Sauppe, M. (ORCID:0000000163123102)↗
DOE OSTI2026

Uncertainties in the production of iron-group nuclides in core-collapse supernovae from Monte Carlo variations of reaction rates

Core-collapse supernovae, occurring at the end of massive star evolution, produce heavy elements, including those in the iron peak. Although the explosion mechanism is not yet fully understood, theoretical models can reproduce optical observations and observed elemental abundances. However, many nuclear reaction rates involved in explosive nucleosynthesis have large uncertainties, impacting the reliability of abundance predictions. To address this, we have previously developed a Monte Carlo-based nucleosynthesis code that accounts for reaction rate uncertainties and has been applied to nucleosynthesis processes beyond iron. Our framework is also well suited for studying explosive nucleosynthesis in supernovae. In this paper, we investigate 1D explosion models using the ‘PUSH method’ , focusing on progenitors with varying metallicities and initial masses around $M_{\rm ZAMS} = 16\, {\rm M}_{\odot }$. Detailed post-process nucleosynthesis calculations and Monte Carlo analyses are used to explore the effects of reaction rate uncertainties and to identify key reaction rates in explosive nucleosynthesis. We find that many reactions have little impact on the production of iron-group nuclei, as these elements are primarily synthesized in the nuclear statistical equilibrium. However, we identify a few ‘key reactions’ that significantly influence the production of radioactive nuclei, which may affect astrophysical observables. In particular, for the production of ${}^{44}{\rm Ti}$, we confirm that several traditionally studied nuclear reactions have a strong impact. However, determining a single reaction rate is insufficient to draw a definitive conclusion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Explicit Runge–Kutta Methods that Alleviate Order Reduction

Explicit Runge–Kutta (RK) methods are susceptible to a reduction in the observed order of convergence when applied to an initial boundary value problem with time-dependent boundary conditions. We study conditions on explicit RK methods that guarantee high order convergence for linear problems; we refer to these conditions as weak stage order conditions. We prove a general relationship between the method’s order, weak stage order, and number of stages. Furthermore, we derive explicit RK methods with high weak stage order and demonstrate, through numerical tests, that they avoid the order reduction phenomenon up to any order for linear problems and up to order three for nonlinear problems.

explicit Runge–Kutta↗
DOE OSTI2024

Data‐driven variational method for discrepancy modeling: Dynamics with small‐strain nonlinear elasticity and viscoelasticity

Abstract The effective inclusion of a priori knowledge when embedding known data in physics‐based models of dynamical systems can ensure that the reconstructed model respects physical principles, while simultaneously improving the accuracy of the solution in the previously unseen regions of state space. This paper presents a physics‐constrained data‐driven discrepancy modeling method that variationally embeds known data in the modeling framework. The hierarchical structure of the method yields fine scale variational equations that facilitate the derivation of residuals which are comprised of the first‐principles theory and sensor‐based data from the dynamical system. The embedding of the sensor data via residual terms leads to discrepancy‐informed closure models that yield a method which is driven not only by boundary and initial conditions, but also by measurements that are taken at only a few observation points in the target system. Specifically, the data‐embedding term serves as residual‐based least‐squares loss function, thus retaining variational consistency. Another important relation arises from the interpretation of the stabilization tensor as a kernel function, thereby incorporating a priori knowledge of the problem and adding computational intelligence to the modeling framework. Numerical test cases show that when known data is taken into account, the data driven variational (DDV) method can correctly predict the system response in the presence of several types of discrepancies. Specifically, the damped solution and correct energy time histories are recovered by including known data in the undamped situation. Morlet wavelet analyses reveal that the surrogate problem with embedded data recovers the fundamental frequency band of the target system. The enhanced stability and accuracy of the DDV method is manifested via reconstructed displacement and velocity fields that yield time histories of strain and kinetic energies which match the target systems. The proposed DDV method also serves as a procedure for restoring eigenvalues and eigenvectors of a deficient dynamical system when known data is taken into account, as shown in the numerical test cases presented here.

Masud, Arif↗
DOE OSTI2024

Bayesian Analysis of TRISO Fuel: Quantifying Model Inadequacy, Incorporating Lower-Length-Scale Effects, and Developing Parallel Active Learning Capabilities

The U.S. Department of Energy (DOE)’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel-cycle systems. This program has been providing engineering-scale support for the continued development of BISON, a high-fidelity, high-resolution fuel performance tool. Fuel behavior in nuclear reactors is governed by a complex network of mechanisms that interact with various other physics aspects in the reactor system. Any model developed to represent fuel behavior will likely be idealized, resulting in uncertainties when comparing their predictions against the observed data. In Fiscal Year (FY)-23, we initiated the Uncertainty Quantification (UQ) work by using Bayesian methods to establish a level of model trustworthiness and further improve it, with a particular emphasis on TRI-Structural isOtropic (TRISO) nuclear fuel. This year, we further expanded on that UQ work by investigating an approach to quantifying model inadequacy and accounting for lower-length scale (LLS) effects in TRISO silver (Ag) release modeling. Furthermore, we are implementing parallel active learning capabilities to reduce the computational cost (i.e., required computational resources and elapsed time) of performing UQ. Specifically, we utilized The Kennedy O’Hagan framework for Bayesian uncertainty quantification (KOH) to account for model inadequacy in TRISO Ag release predictions made by BISON. The KOH framework represents an improvement over the standard Bayesian framework used in FY-23. Explicitly accounting for model inadequacy in the Bayesian framework helps establish the level of experimental noise uncertainty in the Advanced Gas Reactor (AGR) data. We compared the inverse UQ results obtained from both the standard Bayesian and KOH frameworks in light of the AGR-2/3/4 data, and also compared the predictive UQ results obtained from these two frameworks in light of the AGR-1 data. Next, we investigated the impact of considering LLS effects in the Ag release simulations. We developed an expanded database of LLS simulated effective diffusivities for Ag, covering a wide range of microstructures and temperatures. Using this database, we developed a framework for incorporating LLS effects into the engineering-scale Ag release UQ. We developed both parametric and non-parametric approaches for bridging the length scales. We then investigated the inverse UQ results in light of the AGR-2/3/4 data and the predictive UQ results in light of the AGR-1 data, and compared the LLS-informed approach and the Arrhenius equation, which does not include microstructure information. Finally, we discussed implementing parallel active learning capabilities in the Multiphysics Object Oriented Simulation Environment (MOOSE)/BISON to reduce the computational cost (i.e., computational resources and elapsed time) of Bayesian UQ. For verification purposes, we first tested these new capabil ities on a species interaction problem. We then demonstrated them on the TRISO Ag release application, showing that parallel active learning capabilities can enhance the accuracy of UQ while also substantially reducing the computational cost in comparison to the reference methods developed in FY-23.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Design workflow of a symmetric traveling wave antenna for fast ion production on DD tokamaks

Initial computational plasma physics scoping and a finite element method antenna modeling design workflow for a symmetric center-fed high-field side high harmonic fast wave traveling wave array (TWA) antenna are reported here. The TWA is designed to generate a test population of fast deuterium ions in an existing D–D tokamak by heating neutral beam deuterium ions, accelerating them from 80 keV to several hundred keV. The resulting fast particles are tailored to mimic key reactor energetic particle parameters with regards to exciting Alfven eigenmode instabilities, allowing for a D–D tokamak like DIII-D or ASDEX-U to replicate reactor-relevant conditions experimentally. Initial scenario scoping for high single-pass absorption as well as good preferential fast ion damping relative to electron damping was completed using the ray-tracing/Fokker–Planck codes GENRAY and CQL3D. Python RF network analysis packages were used to create a custom TWA optimization tool to inform a COMSOL flat antenna design, and Petra-M was used to study cold plasma effects. The TWA produced by this workflow has several novel features when compared to previous TWA studies, including symmetric center feeding, and passive end straps for image current cancellation for reduced impurity production. We show here that the antenna design workflow can readily produce TWA antennas optimized for reflection coefficient, image current cancellation, and launched power spectrum shape; and that a population of fast ions can be generated in the correct region of parameter space, warranting future more detailed studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗
DOE OSTI2025

Initial Uncertainty Analysis of Carbon Tetrachloride Contamination and Remediation in the Ringold A and Lower Mud Units at the Central Plateau

The long-term effectiveness of groundwater cleanup at the Hanford Site Central Plateau depends on predictive models that can capture key uncertainties in contaminant fate and transport. Carbon tetrachloride (CCl 4 ), a persistent and toxic compound, presents particular challenges due to variability in degradation rates, uncertainty in initial plume distribution, and subsurface heterogeneity. These uncertainties directly influence plume persistence, migration pathways, and remedy performance, and thus must be systematically evaluated to support long-term remediation planning. To address these gaps, a large-scale Monte Carlo analysis was conducted using the Plateau to River (P2R) model framework. The modeling approach parameterized three primary uncertainty factors: (1) degradation rate, (2) initial plume distribution, and (3) hydraulic conductivity. Degradation was represented as a first-order process, with half-lives ranging from 70 to 700 years. Initial plume distributions were created using a geostatistical simulation method (sgsim), which generates many equally plausible versions of how contaminants might be distributed underground. From this, 100 different scenarios were mapped onto the P2R grid. Variability in hydraulic conductivity was represented in a similar way, with 100 scenarios each for the Ringold Lower Mud and Ringold A units (layers 6 and 7), based on fitted exponential variograms and conditioned to well data. In total, more than 1000 realizations were simulated to assess plume behavior under uncertainty. Results demonstrate that degradation kinetics exert the strongest control over plume persistence: Shorter half-lives produced rapid mass reduction, while longer half-lives yielded persistent plumes with limited attenuation. A nonlinear response was observed, with steep mass reductions at half-lives greater than 200 years and near-linear declines beyond this threshold, reflecting interactions between degradation and pumping. The initial plume distribution strongly influenced early transport patterns, with broader sources generating larger plume footprints, although pump-and-treat operations constrained plume migration to managed areas. By comparison, hydraulic conductivity variability in the Ringold units had only a secondary influence, modifying spreading behavior without altering the dominant migration pathways governed by source configuration and hydraulic controls. Overall, the analysis highlights that uncertainty in degradation rate and initial plume configuration are the primary drivers of variability in plume predictions, while conductivity heterogeneity plays a limited role. These findings underscore the need for improved site-specific data on degradation processes and source characterization to enhance the reliability of long-term performance assessments and to better inform remedial decision-making at the Central Plateau.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Development of a Framework and Methodology for an Advanced Reactor Materials Environmental Effects Design Guide

Advanced non-light-water reactor components may operate at elevated temperature while experiencing cyclic loading, significant neutron irradiation, and exposure to reactor coolant. ASME Boiler and Pressure Vessel Code, Section III, Division 5, provides design rules for elevated-temperature service but does not include specific procedures to account for environmental effects on material properties. This report develops an initial framework and methodology for an Environmental Effects Design Guide (EEDG) focused on neutron irradiation; coolant-environment effects are reserved for future work. The proposed approach treats irradiation as a property-based overlay on the existing Division 5 design process, with two routes: a sparse-data route applying two reduction factors — FCR on creep-rupture strength and FF on fatigue life — for the creep-fatigue evaluations that typically control the design of advanced high-temperature reactor components, and a fuller framework developing the property-to-rule chain across the four Division 5 checks (primary load, strain limits and ratcheting, creep-fatigue, and buckling), together with swelling and weldments as scope items. Both routes are scoped by an in-pile qualification that restricts the use of post-irradiation-examination-derived properties in regimes where an in-pile mechanism could control the design outcome. Illustrative outputs derived on a compiled annealed Type 316 database — FCR ≈ 0.78–0.86 and FF ≈ 0.4 — demonstrate the calculation method within that specific dataset. The framework is an initial, testable design-rule concept; it identifies a practical path for preliminary design evaluations under sparse data and the material data and testing needed to develop the framework further.

Barua, Bipul (ORCID:0000000247184113)↗
DOE OSTI2025

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↗