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DOE OSTI2024

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗

DOE OSTI2024

A Fast Algebraic Multigrid Solver and Accurate Discretization for Highly Anisotropic Heat Flux I: Open Field Lines

We present a novel solver technique for the anisotropic heat flux equation, aimed at the high level of anisotropy seen in magnetic confinement fusion plasmas. Such problems pose two major challenges: (i) discretization accuracy and (ii) efficient implicit linear solvers. We simultaneously address each of these challenges by constructing a new finite element discretization with excellent accuracy properties, tailored to a novel solver approach based on algebraic multigrid (AMG) methods designed for advective operators. We pose the problem in a mixed formulation, introducing the directional temperature gradient as an auxiliary variable. The temperature and auxiliary fields are discretized in a scalar discontinuous Galerkin space with upwinding principles used for discretizations of advection. We demonstrate the proposed discretization’s superior accuracy over other discretizations of anisotropic heat flux, achieving error 1000x smaller for anisotropy ratio of 10 9 , for closed field lines. The block matrix system is reordered and solved in an approach where the two advection operators are inverted using AMG solvers based on approximate ideal restriction, which is particularly efficient for upwind discontinuous Galerkin discretizations of advection. To ensure that the advection operators are nonsingular, in this paper we restrict ourselves to considering open (acyclic) magnetic field lines for the linear solvers. We demonstrate fast convergence of the proposed iterative solver in highly anisotropic regimes where other diffusion-based AMG methods fail.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change

Abstract. This study investigated the computational benefits of using multi-fidelity statistical estimation (MFSE) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario. The goal of this study was to determine whether MFSE can use multiple models of varying cost and accuracy to reduce the computational cost of estimating the mean and variance of the projected mass change of a glacier. The problem size and complexity were chosen to reflect the challenges posed by future continental-scale studies while still facilitating a computationally feasible investigation of MFSE methods. When quantifying uncertainty introduced by a high-dimensional parameterization of the basal friction field, MFSE was able to reduce the mean-squared error in the estimates of the statistics by well over an order of magnitude when compared to a single-fidelity approach that only used the highest-fidelity model. This significant reduction in computational cost was achieved despite the low-fidelity models used being incapable of capturing the local features of the ice-flow fields predicted by the high-fidelity model. The MFSE algorithms were able to effectively leverage the high correlation between each model's predictions of mass change, which all responded similarly to perturbations in the model inputs. Consequently, our results suggest that MFSE could be highly useful for reducing the cost of computing continental-scale probabilistic projections of sea-level rise due to ice-sheet mass change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

An alternative electrode design for accurate spectral induced polarization measurements in variably saturated porous media

SUMMARY Applications of spectral induced polarization (SIP) require electrodes that maintain hydrologic contact with surrounding soils to capture small electrical responses, often observed as phase shifts in milliradians. For unsaturated soils, electrodes must overcome increased electrical contact impedance due to reduced pore fluid. Traditional designs use a ceramic membrane electrode (CME) with a water reservoir and metal conductor, requiring periodic maintenance to retain electrolytic solution. For field applications where maintenance is impractical, alternative designs are needed. This study evaluated a new electrode design (silica flour electrode, SFE) alongside a CME. SFEs use packed silica flour to store water via capillary forces against a metal conductor. This study examined both designs in three variably saturated soils at soil suctions up to 700 mbar and soil water contents below 1 percent, with SIP measurements across 0.01 to 10 000 Hz frequencies. SFEs match CMEs at high frequencies and perform better at lower frequencies, without requiring ongoing maintenance, making them ideal for field use. In water-only experiments, CMEs produced errors and high noise below 1.5 Hz, whereas SFEs were more accurate. However, CMEs performed better above 300 Hz. In fine sand, SFEs performed better due to the relatively lower contact impedance as compared to CMEs. Both electrode types performed comparably in silty sand and silt loam soils, although CMEs required ongoing maintenance, suggesting potential for long-term reliability issues.

58 GEOSCIENCES↗

DOE OSTI2024

Towards Full Field-of-View Fourier Ptychography for Extreme Ultraviolet Microscope

We evaluate various Fourier ptychographic microscopy (FPM) reconstruction algorithms using both simulated and experimental data acquired from an Extreme Ultraviolet (EUV, 13.5 nm wavelength) microscope. We specifically focus on the algorithms' ability to robustly address field-dependent aberrations, which enables increased spatial resolution and quantitative phase imaging across an expanded field of view. We systematically compare the algorithms' performance under aberrations for a single zoneplate imaging system, utilizing Fourier Ring Correlation (FRC) as a systematic metric for assessing reconstruction quality. Furthermore, we explore the impact of systematic errors on the reconstruction of experimental data, aiming to increase the effective field of view by 25-fold, from the nominal 5x5 um2 diffraction-limited area. Additionally, our evaluation incorporates innovative FPM-adjacent methodologies, including the Angular Ptychographic Imaging with Closed-form method (APIC), for reconstructing EUV images.

Gu, Chaoying↗

DOE OSTI2025

Intercomparison of Deep Learning Model Architectures for Atmospheric River Prediction

With a rapid surge in the application of machine learning (ML) for a diverse range of tasks in climate science, the present study addresses a challenge for climate scientists when selecting the optimal ML or deep learning (DL) architecture for a given application. In particular, a DL intercomparison study was performed with a focus on forecasting the position of atmospheric rivers (ARs) on short-range time scales (up to 5-day lead times). AR predictions from multiple DL architectures, including various types of convolutional autoencoders and a vision transformer (ViT), were compared against ECMWF ERA5 reanalysis and hindcasts from a global climate model. DL models with similar trainable parameters were trained on ERA5 reanalysis data and AR positions derived from a thresholding algorithm to ensure a fair comparison among the DL models. Each model’s performance and accuracy in forecasting AR location and key input fields within a 5-day window were assessed using metrics of root-mean-square error, anomaly correlation, and mean intersection over union. The ViT architecture outperformed other autoencoder models in most of the metrics. Incorporating additional meteorological fields only yielded slight improvements in forecasting certain fields at longer lead times. The results also suggest that a smaller number of input time steps or smaller number of autoregressive steps can achieve better prediction skills, while also improving the overall computational efficiency. This research offers valuable insights into the strengths and weaknesses of different DL techniques for AR forecasting, hopefully guiding the development of improved models for forecasting this phenomenon.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Studying electroweak few-body observables in chiral effective field theory

The use of nuclei to study electroweak probes is becoming increasingly relevant experimentally. The success of dark matter and neutrino experiments strongly depends on the ability to control nuclear effects in order to extract the fundamental parameters associated with external probes. Therefore, reliable theoretical calculations of nuclear structure and reactions, with well-controlled errors, are crucial for the success of experimental efforts. Currently, chiral effective field theory ($\chi$EFT) coupled with {\it ab-initio} methods represents one of the best approaches that fulfills these requirements. To use this approach as a tool for studying fundamental physics, it is essential to validate it against experimental data for which the calculations are well under control, such as the elastic scattering of electrons on nuclei. In this proceeding, I will present recent developments in the fitting of electromagnetic currents derived using $\chi$EFT and the calculation of electromagnetic form factors of light nuclei. The results of these calculations demonstrate the strength of the theory in describing the interaction of nuclei with electromagnetic probes over a broad range of momentum transfers and highlight the robustness of $\chi$EFT for analyzing future experimental data aimed at extracting fundamental parameters.

Gnech, Alex [Old Dominion Univ., Norfolk, VA (Unit↗

DOE OSTI2025

Untangling Sources of Error in the Density-Functional Many-Body Expansion

The many-body expansion provides a framework for data-driven applications of electronic structure theory, including parametrization of classical force fields and machine learning. In this article, we demonstrate that its use significantly amplifies quadrature grid errors when modern density-functional approximations are employed. Standard grids that work well in conventional density-functional calculations result in runaway error accumulation when used with the many-body expansion. At the same time, delocalization error is also exacerbated, leading to exaggerated estimates of nonadditive n-body interactions. This is illustrated for anion–water clusters using the SCAN, r2SCAN, ωB97X-V and ωB97M-V functionals. By employing dense quadrature grids, the inherent self-interaction error is exposed, which can then be mitigated using a variety of other strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Photon (Non)Conservation in the Reduced Speed of Light Approximation and How to (Almost) Fix It

The "Reduced Speed of Light" (RSL) approximation is commonly used to speed up radiative transfer calculations in cosmological simulations. However, it has been shown previously that the RSL approximation leads to photon non-conservation when the radiation field is rapidly evolving in time. I show that these missing photons can be counted exactly for some numerical schemes. Adding them back into a simulation, however, is a much harder task. I show one example of such a scheme, which achieves sub-percent accuracy on simple tests. Unfortunately, the scheme performs much worse on semi-realistic simulations of cosmic reionization, leading to a faster overlap and significant errors in the point-wise comparison of the RSL radiation field with the reference simulation that maintains the full speed of light for the radiative transfer.

Gnedin, Nickolay Y. [Fermilab; Chicago U., KICP; C↗

DOE OSTI2025

Exploring the Early Lightning Notification of an Electric Field Mill at the Savannah River Site

At the Savannah River Site (SRS), employees receive automated broadcast notification about lightning only after three strikes have already occurred near the site boundary. To increase employee safety, it is preferential to give employees lead time before lightning strikes occur. We compared measurements from an on-site electric field mill to lightning detection data from the National Lightning Detection Network and the Geostationary Lightning Mapper. Using a difference threshold, we determined that the electric field mill provided a lead time greater than 6 minutes for 95% of lightning events from 2009-2020, with an average lead time of 56 minutes. Detection of events were limited to a 7-mile radius around the field mill. We also identified that the field mill threshold generated many false detections not clearly identified. False detections and detections from only precipitation can be reduced by using a second threshold without creating too many missed detections (Type II errors). The two thresholds used together provide the best information about rapidly changing electric fields and aid in advanced detection necessary to improve the lightning warning system used at SRS.

42 ENGINEERING↗

DOE OSTI2025

Prediction of Alpha-Particle-Immune Gate-All-Around Field-Effect Transistors (GAA-FET) Based SRAM Design

Alpha particles are known to be a major source of particles creating soft errors in semiconductor devices, such as content flipping in Static Random-Access Memory (SRAM). Recent advancements in transistor nodes have led to the introduction of Gate-All-Around Field Effect Transistors (GAA-FETs), which have better gate control, thus better electrostatics. Moreover, the introduction of bottom dielectric isolation (BDI) eliminates substrate leakage and thus is expected to enhance its radiation hardness. It is thus important to explore if one can design an SRAM that is completely radiation-hard to alpha particles. In this paper, using 3D Technology Computer-Aided-Design (TCAD) simulations, we show that it is possible to design an SRAM using GAA-FET technology so that it is immune to single alpha particle radiation error. In other words, with the design, there will be no single-event upset (SEU) due to alpha particles. We first use ab initio calculations in PHITS to show that there is a maximum linear energy transfer (LET), LET max , for the alpha particle in Si and Si x Ge 1-x . Based on that, by de signing a sub-7nm GAA-FET-based SRAM with BDI, we show that the SRAM does not flip even if the particle strike is in the worst-case scenario for LET > LET max .

42 ENGINEERING↗

DOE OSTI2025

A 3D helical filament surrogate model for 3D tokamak equilibria

A novel approach for efficient representation of three-dimensional (3D) tokamak equilibria is investigated, where a set of helical current filaments occupying the plasma region are employed to resolve deviations from the two-dimensional (2D) axi-symmetric state. A discrete set of 3D filaments, located at rational surfaces for a given toroidal mode number n and following the 2D equilibrium field lines (thus forming closed current loops), are found to provide a surrogate model of 3D equilibria with reasonable accuracy. Specifically, application of the filament model to 3D perturbed equilibria, due to the resonant magnetic perturbation (RMP) in DIII-D and MAST-U discharges, reveals that (1) a single helical filament per rational surface is sufficient; (2) 21 such helical filaments are capable of representing the n = 2 3D response field in MAST-U with less than 10% relative error as compared to that computed by a full magnetohydrodynamic code; (3) optimizing currents (both amplitude and phase) flowing in 3D filaments with fixed geometry, the highest accuracy fitting is found to depend on the characteristics of the 3D equilibria such as the coil current phasing of the RMP coils in our case studies. Here, whis filament approach is also applicable for generating surrogate models of other type of 3D tokamak equilibria, including those during the initial phase of the plasma disruption.

MARS-F↗

DOE OSTI2024

Advocating Feedback Control for Human-Earth System Applications

This paper proposes a feedback control perspective for Human-Earth Systems (HESs) which essentially are complex systems that capture the interactions between humans and nature. Recent attention in HES research has been directed towards devising strategies for climate change mitigation and adaptation, aimed at achieving environmental and societal objectives. However, existing approaches heavily rely on HES models, which inherently suffer from inaccuracies due to the complexity of the system. Moreover, overly detailed models often prove impractical for optimization tasks. We propose a framework inheriting from feedback control strategies the robustness against model errors, because inaccuracies are mitigated using measurements retrieved from the field. The framework comprises two nested control loops. The outer loop computes the optimal inputs to the HES, which are then implemented by actuators controlled in the inner loop. Potential fields of applications are also identified and a numerical example is provided.

biological system modeling↗

DOE OSTI2025

Hybrid Quantum Networks with Discrete Polarizations and Continuous Quadrature Variables

Final Scientific/Technical Report for DOE Project DE-SC0022069. Quantum communication and computation systems have evolved along two largely independent paradigms: discrete-variable (DV) systems that encode information in qubits such as photon polarizations or photon-number states, and continuous-variable (CV) systems that encode information in optical field quadratures. Each approach offers distinct advantages—DVs provide low error rates and compatibility with single-photon platforms, while CVs support deterministic operations and efficient quantum state manipulation. A fundamental challenge for building a scalable Quantum Internet lies in interfacing these two regimes into a unified hybrid architecture that can coherently distribute and process quantum information across heterogeneous quantum nodes. This project aims to develop and demonstrate a hybrid optical quantum network that seamlessly integrates DV and CV systems. Specifically, we investigate a new class of hybrid entanglement between the discrete polarizations of single photons and the continuous quadrature variables of optical cat states, overcoming incompatibilities in existing DV–CV demonstrations. Using this new entanglement resource, the team investigates a multi-node hybrid quantum local area network (Q-LAN) testbed capable of DV–CV entanglement generation, swapping, and distribution across fiber links.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2025

Propagating information content: an example with advection

The mathematical algorithm to derive geophysical information from remote sensing observations is called a retrieval. The mathematics of many retrieval problems are ill-posed, and thus a priori information is used to help constrain the derived geophysical variable to realistic values. One quantity of interest, therefore, is the information content of the observation. Perfect information content in the observation would be achieved if the retrieval were able to capture any perturbation in the desired geophysical variable with the proper magnitude. Many new data products can be derived by combining geophysical variables retrieved from multiple different remote sensors. This paper explores, for the first time, how to derive the information content of these derived products. The approach uses traditional error propagation techniques to derive the uncertainty of the derived field twice, both when the observations are used in the retrieval and also when only the a priori information from each remote sensor is propagated. These two uncertainties are then used to provide an estimate of the information content of the derived geophysical variable. This study demonstrates how to propagate the uncertainties from six different instruments to provide the information content for water vapor and temperature advection. A multi-month analysis demonstrates that, in a mean sense, the information content for temperature advection is nearly unity for all heights below 700 m while, the information content for water vapor advection is somewhat more variable but still larger than 0.6 in the convective boundary layer.

Turner, David D. [National Oceanic and Atmospheric↗

DOE OSTI2026

Higher-order Zeno sequences

The quantum Zeno effect typically refers to freezing the dynamics of a quantum system through frequent observations. In general, quantum Zeno dynamics is obtained with an error of order 𝒪⁢(1/𝑁), where 𝑁 is the number of projective measurements performed within a fixed evolution time. In this work, we develop higher-order Zeno sequences that achieve faster convergence to Zeno dynamics, yielding an improved error scaling of 𝒪⁢(1/𝑁 2⁢𝑘 ), where 𝑘 describes the order of the Zeno sequence. This is achieved by relating higher-order Zeno sequences to higher-order Trotter formulas that achieve similar convergence behavior. We leverage this relation to develop higher-order Zeno sequences for different manifestations of the quantum Zeno effect, including frequent projective measurements and unitary kicks. We go on to discuss achieving quantum Zeno dynamics through periodic control fields of high frequency. We explicitly develop control fields that yield a second-order type improvement in the Zeno error scaling and present shorter Zeno sequences. Finally, we discuss the connection to randomized and Uhrig dynamical decoupling to develop more efficient implementations in the weak-coupling regime.

Quantum Zeno dynamics↗

DOE OSTI2024

Beam correction for multi-pass arcs in FFA@CEBAF: status update

This work examines the multi-pass steering of six electron beams in an FFA arc ranging from approximately 10.5 GeV to 22 GeV. Shown here is an algorithm based on singular value decomposition (SVD) to successfully steer all six beams through the arc given precise knowledge of all beam positions at each of one hundred and one diagnostic locations with one hundred individual corrector magnets: that is successive application of SVD to different 100 × 101 response matrices—one for each beam energy. Further, a machine learning scheme is developed which only requires knowledge of the energy-averaged beam position at each location to provide equivalent steering. Extension of this scheme to other beam optics quantities as well as transverse and longitudinal coupling is explored.

Accelerator Physics↗

DOE OSTI2024

Manufactured solutions for an electromagnetic slot model

The accurate modeling of electromagnetic penetration is an important topic in computational electromagnetics. Electromagnetic penetration occurs through intentional or inadvertent openings in an otherwise closed electromagnetic scatterer, which prevent the contents from being fully shielded from external fields. To efficiently model electromagnetic penetration, aperture or slot models can be used with surface integral equations to solve Maxwell's equations. A necessary step towards establishing the credibility of these models is to assess the correctness of the implementation of the underlying numerical methods through code verification. Surface integral equations and slot models yield multiple interacting sources of numerical error and other challenges, which render traditional code-verification approaches ineffective. In this paper, we provide approaches to separately measure the numerical errors arising from these different error sources for the method-of-moments implementation of the electric-field integral equation with a slot model. Finally, we demonstrate the effectiveness of these approaches for a variety of cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗