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DOE OSTI2025

Deep learning model for fast, science-based forecasting of fluid migration along faults in geologic carbon storage scenarios

Effective long-term geologic storage depends on robust site selection and credible, science-based forecasting of subsurface behavior to ensure storage integrity. For this work, we develop a deep learning–based reduced-order model (ROM) to quantify potential carbon dioxide (CO₂) and brine migration through geological faults. The ROM combines a Transformer model for binary classification and a Stacked Ensemble for regression, trained on a comprehensive dataset generated from 1400 physics-based reservoir simulations. Key geologic and operational parameters—including fault geometry, reservoir structure, and injection conditions—were systematically varied to capture a wide range of fluid migration scenarios. The ROM accurately predicts the onset of migration, cumulative migration volumes of both CO₂ and brine, and associated migration rates, as compared to an independent set of validation simulations, while significantly reducing computational cost compared to traditional simulation methods. Model performance was evaluated across diverse fault configurations, revealing that shallow reservoir geometry and fault angle are among the most influential factors governing migration behavior. Sensitivity analysis using SHapley Additive exPlanations (SHAP) provided interpretability, revealing distinct patterns in how geological and operational features drive transient versus cumulative migration outcomes. The ROM’s ability to rapidly simulate fault migration scenarios enables efficient sensitivity analyses, scenario evaluations, and decision support for site selection and monitoring design. This approach enhances the safety, scalability, and long-term operational performance of geologic carbon storage (GCS) systems by providing a robust, interpretable tool for predicting subsurface fluid migration and assessing fault-related migration potential.

42 ENGINEERING↗

DOE OSTI2025

Influence of background sources and topographic resolution in the Weather Research and Forecasting Model on xenon plume characteristics at monitoring stations

For many atmospheric monitoring applications, networks of measurement sites—such as the radionuclide stations of the International Monitoring System—can be sparse. With measurement locations potentially hundreds to thousands of kilometers from a release it is important to quantify the effects of physical processes on transport and dispersion of plumes between source and measurement locations. This study addresses the effects of background sources and topography resolution near the release location of radionuclides. We use the Weather Research and Forecasting (WRF) model with inline chemistry to investigate (1) how an additional, time-varying source of 133 Xe, such as an operational medical isotope production facility, contributes to activity concentration measurements at monitoring sites, and (2) how complex topography influences on atmospheric conditions near emission sources impact plume concentrations at varying distances from the source. Two 133 Xe emission sources, including (1) a high flux rate of short duration representing an explosive event, and (2) a variable and continuous background source, are simulated. The continuous background source contributes significantly to total 133 Xe concentrations at several monitoring stations. Further, a WRF simulation at 9 km horizontal resolution is compared with a nested grid simulation, where the innermost domain has a resolution of 1 km. Increased topographic resolution leads to an improved representation of plume responses to local winds, with topographic influences greatest at locations closest to the sources. Differences between the two domain resolutions decrease at greater distances from the sources, as plumes have time to spread and mix and are influenced by synoptic scale circulation patterns that are represented similarly in both simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Artificial intelligence time series forecasting for feed-forward laser stabilization

Laser plasma accelerators, typically operating at 1–10 Hz repetition rates, have the ability to produce high-quality electron beams in compact, all-optical-driven configurations, with the electron beams uniquely suited for a wide variety of accelerator-based applications. However, fluctuations and drifts in the laser delivery to the meter-scaled and below plasma target (the electron beam source) will translate into electron beam source variations that can limit their utility for demanding applications like light sources or linear colliders. Commercially available active feedback laser stabilization systems are intrinsically bandwidth limited due to their integration with multi-inch corrective mirror mounts which minimizes their effectiveness. In this manuscript, we present a Neural Network time series forecaster that can predict laser position fluctuations of the laser delivery to the final target well ahead of a future laser shot. The Root-Mean-Square-Error (RMSE) of the prediction accuracy was < 2 μ m for a 1 / e 2 beam radius of 34 μ m . Our feed-forward approach serves as a first-step in circumventing the bandwidth limitations imposed by the currently available stabilization systems since it allows for mirrors to be moved into position ahead of time to offset the predicted future position drift. This will help advance laser plasma accelerator research by providing greater robustness and stability needed for its applications.

Berger, Curtis↗

DOE OSTI2023

Impacts of hybridization and forecast errors on the probabilistic capacity credit of batteries

Battery storage is increasingly identified as being among the least-cost mix of technologies in the evolving U.S. electricity mix. This study explores the marginal capacity credit of batteries using a probabilistic, reliability-based, effective firm capacity method, which we apply for multiple battery power ratings, durations, coupling types, deployment locations, and dispatch profiles within a test system that is based on the Texas Interconnection in the year 2024. We find that the capacity credits for all battery durations depend on their ability to predict the timing of reliability events. Even 1-2 h forecast errors - resulting in early or delayed battery discharging relative to the onset of a reliability event - lead pronounced capacity credit reductions, especially for 4-h duration batteries. Coupling batteries with solar mitigates the uncertainty associated with a shorter-duration battery's availability during reliability events, primarily due to the relatively high solar capacity credit in our test system. Coupled (or hybrid) system designs with oversized solar arrays, the ability to charge the coupled battery with grid energy, and larger batteries lead to the greatest capacity credit benefits of hybridization. We do not see evidence that the hybrid capacity credit exceeds the sum of the separate battery and solar capacity credits.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Calibration of cloud and aerosol related parameters for solar irradiance forecasts in WRF-solar

Model parameters are a major source of uncertainty in numerical weather prediction. Recently, the Weather Research and Forecasting model with Solar extensions (WRF-Solar) has been upgraded by enhancing the treatment of sub-grid scale cloud and aerosols with augmentations of a sub-grid scale cloud scheme (CLD3) and an upgraded aerosol-aware Thompson-Eidhammer scheme (TE14). However, the value of model parameters associated with these parameterizations are assigned based on limited measurements or theoretical calculations. Calibrating the most sensitive parameters has the potential to improve solar irradiance predictions. Here, we adopted a multiobjective surrogate-based optimization (SBO) framework to calibrate nine parameters used in CLD3 and TE14 that lead to the largest sensitivity in simulated irradiance. The normalized mean-absolute-error (NMAE) of global horizontal irradiance (GHI) and direct normal irradiance (DNI) are minimized by calibrating WRF-Solar over two regions including the Southern Great Plains (SGP) and Central California, in order to focus on parameter calibration under cloudy conditions with different aerosol loading. The results show that generalized linear model (GLM)-based surrogate models approximate physical models well, particularly when the third order and three-way interaction terms are considered. The SBO framework efficiently searches the parameter space for optimal solutions with less computational costs than directly calibrating the physical model. We first calibrate CLD3 parameters over the less-polluted SGP region. Optimized CLD3 parameters alone result in NMAE reduction by 14% for the site-mean and up to 33% for individual cases over the SGP region. With further calibration of TE14 parameters over the Central California during active fire periods, the optimized parameters lead to over 20% reductions of NMAE. Our investigation reveals, however, that optimizing TE14 has a limited impact on irradiance simulations under less-polluted conditions in the SGP.

14 SOLAR ENERGY↗

DOE OSTI2025

Propagating synthetic populations with dynamic Bayesian networks: a framework for long-horizon demographic forecasting

This study presents a dynamic demographic microsimulator using dynamic Bayesian networks to forecast long–term changes in household and individual life events. Leveraging longitudinal Panel Study of Income Dynamics (PSID) data, two networks for individuals and households were modeled to simulate transitions in employment, income, education, marriage, childbirth, leaving the parental home, home ownership, mortality, and household formation or dissolution. Across 1,000 simulation runs spanning 24 years, household–level outcomes remain highly accurate and individual–level predictions reasonable. Although accuracy naturally declines with projection horizon, performance remains promising at both levels. This study addresses a key limitation of existing population synthesis models, which typically generate only a single static snapshot of the population. In conclusion, by introducing a framework that propagates cross-sectional outputs into the future, the microsimulator enables the tracking of demographic evolution over time, enhances realism in population-based simulations, and supplies credible inputs to agent-based travel demand models.

Demographic modeling↗

DOE OSTI2025

Forecasting Battery Electrode Performance via Electrochemical Fluorescence Microscopy and Machine-Learning

Predicting lithium-ion battery performance is hindered by microscale electrode heterogeneities invisible to conventional diagnostics. Here, we combine electrochemical fluorescence microscopy (EFM), which maps electronic connectivity by visualizing an electrofluorophore reaction distribution, with a multitask ElasticNet regression to forecast discharge capacity from spatial heterogeneity. Analyzing 196 images from six pilot-scale LiNi 0.5 Mn 0.3 Co 0.2 O 2 cathodes with varying carbon loadings, we extract 62 descriptors that capture morphology and texture. A compact five-feature model predicts capacity across eight discharge rates, achieving a per-target R 2 of up to 0.63 and an overall R 2 of 0.92, with a mean absolute percentage error of less than 2%. This performance rivals impedance-based approaches while avoiding their reliance on postformation data and incomplete electronic network information. Our facile and rapid, image-driven method may enable electrode quality control upstream of costly cell assembly to offer a transformative tool for data-driven battery research and manufacturing.

battery electrodes↗

DOE OSTI2021

Improving the MJO Forecast of S2S Operation Models by Correcting Their Biases in Linear Dynamics

The operational dynamic subseasonal to seasonal (S2S) models for Madden-Julian oscillation (MJO) forecasting mostly still suffer from systematic errors in capturing the MJO's key dynamic features, such as its growth rate and propagation speed. By deriving the linear dynamic operators using the linear inverse modeling (LIM) approach, we propose a method to partly correct the errors in MJO linear dynamic operators to improve the MJO predictions of three operational dynamic S2S models. Correcting the deficiencies of the too-fast decay rates and the unrealistic propagating phase speeds lead to MJO prediction skills being extended by approximately 2–4 days. The improvements are more significant for the models with larger biases in MJO amplitude and propagation. This approach in principle may be extendable to predictions of other types of climate variability such as ENSO on one hand, and possible inclusions of nonlinear dynamics effects on the other hand.

58 GEOSCIENCES↗

DOE OSTI2024

Forecasting Tropical Annual Maximum Wet-Bulb Temperatures Months in Advance From the Current State of ENSO

Humid heatwaves, characterized by high temperature and humidity combinations, challenge tropical societies. Extreme wet-bulb temperatures (TW) over tropical land are coupled to the warmest sea surface temperatures by atmospheric convection and wave dynamics. Here, we harness this coupling for seasonal forecasts of the annual maximum of daily maximum TW (TW max ). We develop a multiple linear regression model that explains 80% of variance in tropical mean TW max and significant regional TW max variances. The model considers warming trends and El Niño and Southern Oscillation indices. Looking ahead, the strong-to-very-strong El Niño at the end of 2023, with an Oceanic Niño Index of ~2.0, suggests a 2024 tropical land mean TW max of 26.2°C (25.9–26.4°C), and a 68% chance (24%–94%) of breaking existing records. This method also predicts regional TW max in specific areas.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Dual‐Transformer Deep Learning Framework for Seasonal Forecasting of Great Lakes Water Levels

Abstract The Great Lakes of North America form one of the largest freshwater systems on Earth, and their lake‐wide average water levels (lake levels) can fluctuate by more than 0.5 m on a seasonal scale. These fluctuations pose substantial challenges for coastal resilience, flood risk management, and navigation planning. Accurate seasonal forecasting of lake levels using traditional mechanistic models is challenging due to the complex physical mechanisms and coupled hydroclimatic processes involved. Recently, deep learning has gained prominence in geoscience applications for its ability to recognize intricate patterns within multiphysical data sets. Here, we introduce a novel Dual‐Transformer deep learning framework, tested on the Great Lakes. This architecture integrates two modified Transformer models: the Prophet, which predicts underlying trends, and the Critic, which refines the Prophet's predictions. The final lake level prediction is derived by weighting the outputs of both models through a multi‐layer perceptron, jointly trained with the Prophet and Critic to enhance overall accuracy. Our results demonstrate that the innovative learning framework achieves the highest prediction accuracy compared to established deep learning models when using identical input features. It attains a root mean square error of 4–7 cm in predicting lake levels up to 6 months in advance across the lakes. Additionally, the Dual‐Transformer model runs six orders of magnitude faster than conventional mechanistic models, producing results in less than one second on a typical personal computer. These findings suggest that our deep learning framework has strong potential to advance lake level prediction and carries important implications for water management and disaster mitigation, thereby enhancing the quality of life in coastal regions.

Chen, Yi [Great Lakes Research Center Michigan Tec↗

DOE OSTI2020

Short-term load demand forecasting through rich features based on recurrent neural networks

With the emerging penetration of renewables and dynamic loads, the understanding of grid edge loading conditions becomes increasingly substantial. Load modelling researches commonly consist of explicitly expressed load models and non-explicitly expressed techniques, of which artificial intelligence approaches turn out to be the major path. This paper reveals the artificial intelligence-based load modelling technique to enhance the knowledge of current and future load information considering geographical and weather dependencies. This paper presents a recurrent neural network based sequence to sequence (Seq2Seq) model to forecast the short-term power loads. Also, a feature attention mechanism, which is along channel and time directions, is developed to improve the efficiency of feature learning. The experiments over three publicly available datasets demonstrate the accuracy and effectiveness of the proposed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Bayesian forecasts for dark matter substructure searches with mock pulsar timing data

Dark matter substructure, such as primordial black holes (PBHs) and axion miniclusters, can induce phase shifts in pulsar timing arrays (PTAs) measurements due to gravitational effects. In order to gain a more realistic forecast for the detectability of such models of dark matter with PTAs, we propose a Bayesian inference framework to search for phase shifts generated by PBHs and perform the analysis on mock PTA data. Furthermore, for most PBH masses the constraints on the dark matter abundance agree with previous (frequentist) analyses (without mock data) to $\mathcal{O}$(1) factors. This further motivates a dedicated search for PBHs (and dense small scale structures) in the mass range from 10 -8 M ⊙ to well above 10 2 M ⊙ with the Square Kilometer Array. Moreover, with a more optimistic set of timing parameters, future PTAs are predicted to constrain PBHs down to 10 -11 M ⊙ . Lastly, we discuss the impact of backgrounds, such as Supermassive Black Hole Mergers, on detection prospects, suggesting a future program to separate a dark matter signal from other astrophysical sources.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Forecasting the potential of weak lensing magnification to enhance LSST large-scale structure analyses

ABSTRACT Recent works have shown that weak lensing magnification must be included in upcoming large-scale structure analyses, such as for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), to avoid biasing the cosmological results. In this work, we investigate whether including magnification has a positive impact on the precision of the cosmological constraints, as well as being necessary to avoid bias. We forecast this using an LSST mock catalogue and a halo model to calculate the galaxy power spectra. We find that including magnification has little effect on the precision of the cosmological parameter constraints for an LSST galaxy clustering analysis, where the halo model parameters are additionally constrained by the galaxy luminosity function. In particular, we find that for the LSST gold sample (i < 25.3) including weak lensing magnification only improves the galaxy clustering constraint on Ωm by a factor of 1.03, and when using a very deep LSST mock sample (i < 26.5) by a factor of 1.3. Since magnification predominantly contributes to the clustering measurement and provides similar information to that of cosmic shear, this improvement would be reduced for a combined galaxy clustering and shear analysis. We also confirm that not modelling weak lensing magnification will catastrophically bias the cosmological results from LSST. Magnification must therefore be included in LSST large-scale structure analyses even though it does not significantly enhance the precision of the cosmological constraints.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

A forecast for large-scale structure constraints on Horndeski gravity with CO line intensity mapping

Here we consider the potential for line intensity mapping (LIM) of the rotational CO(1-0), CO(2-1), and CO(3-2) transitions to detect deviations from General Relativity from 0 < z < 3 within the framework of a very general class of modified gravity models, called Horndeski’s theories. Our forecast assumes a multitracer analysis separately obtaining information from the matter power spectrum and the first two multipoles of the redshift space distortion power spectrum. To achieve ±0.1 level constraints on the slope of the kinetic gravity braiding and Planck mass evolution parameters, a mm-wave LIM experiment would need to accumulate ≈10 8 –10 9 spectrometre-hours, feasible with instruments that could be deployed in the 2030s. Such a measurement would constrain the parameters of Horndeski’s theory at a level at worst competitive to and at best an order of magnitude tighter than existing constraints from the CMB and LSS. Our modelling code is publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Forecasting constraints on the mean free path of ionizing photons at z ≥ 5.4 from the Lyman-α forest flux autocorrelation function

ABSTRACT Fluctuations in Lyman-α (Ly α) forest transmission towards high-z quasars are partially sourced from spatial fluctuations in the ultraviolet background, the level of which are set by the mean free path of ionizing photons (λmfp). The autocorrelation function of Ly α forest flux characterizes the strength and scale of transmission fluctuations and, as we show, is thus sensitive to λmfp. Recent measurements at z ∼ 6 suggest a rapid evolution of λmfp at z > 5.0 which would leave a signature in the evolution of the autocorrelation function. For this forecast, we model mock Ly α forest data with properties similar to the XQR-30 extended data set at 5.4 ≤ z ≤ 6.0. At each z, we investigate 100 mock data sets and an ideal case where mock data matches model values of the autocorrelation function. For ideal data with λmfp = 9.0 cMpc at z = 6.0, we recover $\lambda _{\text{mfp}}=12^{+6}_{-3}$ cMpc. This precision is comparable to direct measurements of λmfp from the stacking of quasar spectra beyond the Lyman limit. Hypothetical high-resolution data leads to a $\sim 40~{{\ \rm per\ cent}}$ reduction in the error bars over all z. The distribution of mock values of the autocorrelation function in this work is highly non-Gaussian for high-z, which should caution work with other statistics of the high-z Ly α forest against making this assumption. We use a rigorous statistical method to pass an inference test, however future work on non-Gaussian methods will enable higher precision measurements.

Astronomy & Astrophysics↗

DOE OSTI2025

Gravitational waves and galaxies cross-correlations: a forecast on GW biases for future detectors

ABSTRACT Gravitational waves (GWs) have rapidly become important cosmological probes since their first detection in 2015. As the number of detected events continues to rise, upcoming instruments like Einstein Telescope (ET) and Cosmic Explorer (CE) will observe millions of compact binary mergers. These detections, coupled with galaxy surveys by instruments such as the Dark Spectroscopic Energy Instrument (DESI), Euclid, and the Vera Rubin Observatory, will provide unique information on the large-scale structure of the universe by cross-correlating GWs with the distribution of galaxies hosting them. In this paper, we focus on how cross-correlations constrain the clustering bias of GWs emitted by the coalescence of binary black holes (BBHs). This parameter links BBHs to the underlying dark matter distribution, hence informing us how they populate galaxies. Using a multitracer approach, we forecast the precision of these measurements under different survey combinations. Our results indicate that current GW detectors will have limited precision, with measurement errors as high as $\displaystyle \sim 50~{{\ \rm per\ cent}}$. However, third-generation detectors like ET, when cross-correlated with Legacy Survey of Space and Time (LSST) data, can improve clustering bias measurements to within 2.5 per cent. Furthermore, we demonstrate that these cross-correlations can enable a per cent-level measurement of the magnification lensing effect on GWs. Despite this, there is a degeneracy between magnification and evolution biases, which hinders the precision of both. This degeneracy is most effectively addressed by assuming knowledge of one bias or targeting an optimal redshift range of $\displaystyle 1 \lt z \lt 2.5$. Our analysis opens new avenues for studying the distribution of BBHs and testing the nature of gravity through large-scale structure.

Zazzera, Stefano (ORCID:0000000158979221)↗

DOE OSTI2025

Forecasting constraints on the high-z IGM thermal state from the Lyman-α forest flux autocorrelation function

ABSTRACT The autocorrelation function of the Lyman-$\alpha$ (Ly $\alpha$) forest flux from high-z quasars probes the small-scale structure of the intergalactic medium (IGM). The thermal state of the IGM, determined by the physics of reionization, sets the small-scale power observed in the Ly $\alpha$ forest. To explore the sensitivity of the autocorrelation function to the IGM’s thermal state, we compute the autocorrelation function from a cosmological hydrodynamical simulation with an instantaneous reionization model and 135 post-processed thermal states. Using mock data sets of 20 quasars, we forecast constraints on $T_0$ and $\gamma$, which characterize the post-processed IGM thermal state, at $5.4 \le z \le 6$. While this model simplifies the IGM’s thermal state, it serves as a key first step in assessing future observational prospects. We also perform an inference test on mocks and re-weight out posterior distributions to guarantee that they exhibit statistically correct behaviour. At $z = 5.4$, we find that an idealized data set constrains $T_0$ to 59 per cent and $\gamma$ to 16 per cent at the 1$\sigma$ equivalent confidence level. To explore more realistic, non-instantaneous reionization scenarios, we analyse four models combining temperature and ultraviolet background (UVB) fluctuations at $z = 5.8$. We find that mock data generated from a model with both temperature and UVB fluctuations can rule out a model with only temperature fluctuations at the $> 1\sigma$ level 73.9 per cent of the time.

Wolfson, Molly↗

DOE OSTI2024

Forecasting fluid-injection induced seismicity to choose the best injection strategy for safety and efficiency

Induced seismicity poses a challenge to the development of Enhanced Geothermal Systems (EGS). Improving monitoring and forecasting techniques is essential to mitigate induced seismicity and thereby fostering a positive perception of EGS projects among local authorities and population. Induced seismicity is the result of complex and coupled thermo-hydro-mechanical-chemical mechanisms. Injection flux and pressure are crucial controlling parameters for both hydraulic stimulation and circulation protocols. We develop a methodology combining a hydro-mechanical model with a seismicity rate model to estimate the magnitude and frequency of mainshocks and aftershocks induced by fluid injection. We apply the methodology to the case of the Basel EGS (2006, Switzerland) to compare the effects of progressive, cyclic and constant injections on the mechanical response of discrete faults. Results from the coupled hydro-mechanical models show that the pore pressure diffusion and consequent enhancement of fault permeability are limited to the vicinity of the injection well during cyclic injection. Additionally, constant injection induces seismicity from the start of the injection but enhances the permeability of most of the faults within a shorter duration, inducing less post-injection seismicity. The methodology can be adapted to any numerical model and allows new projects to be developed by anticipating the safest injection protocol. This article is part of the theme issue ‘Induced seismicity in coupled subsurface systems’.

58 GEOSCIENCES↗