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Results for “surrogate approximator”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions

Particle collisions at accelerators like the Large Hadron Collider (LHC), recorded by experiments such as ATLAS and CMS, enable precise standard model measurements and searches for new phenomena. Simulating these collisions significantly influences experiment design and analysis but incurs immense computational costs, projected at millions of CPU-years annually during the high luminosity LHC (HL-LHC) phase. Currently, simulating a single event with Geant4 consumes around 1000 CPU seconds, with calorimeter simulations especially demanding. To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). Our RBM architecture is tailored for D-Wave’s Pegasus-structured advantage quantum annealer for sampling, leveraging the flux bias for conditioning. This approach combines classical RBMs as universal approximators for discrete distributions with quantum annealing’s speed and scalability. We also introduce an adaptive method for efficiently estimating effective inverse temperature, and validate our framework on Dataset 2 of CaloChallenge.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Towards robust surrogate models: Benchmarking machine learning approaches to expediting phase field simulations of brittle fracture

Data-driven approaches have the potential to make modeling complex, nonlinear physical phenomena significantly more computationally tractable. For example, computational modeling of fracture is a core challenge where machine learning techniques have the potential to provide a much needed speedup that would enable progress in areas such as multi-scale modeling and uncertainty quantification. Currently, phase field modeling (PFM) of fracture is one such approach that offers a convenient variational formulation to model crack nucleation, branching and propagation. To date, machine learning techniques have shown promise in approximating PFM simulations. While standard fracture benchmarks represent realistic scenarios frequently observed in practice, they typically do not provide sufficiently challenging tests for data-driven methods. Here, to address this gap, we introduce a challenging dataset based on PFM simulations designed to benchmark and advance ML methods for fracture modeling. This dataset includes three energy decomposition methods, two boundary conditions, and 1000 random initial crack configurations for a total of 6000 simulations. Each sample contains 100 time steps capturing the temporal evolution of the crack field. Alongside this dataset, we also implement and evaluate Physics Informed Neural Networks (PINN), Fourier Neural Operators (FNO), and UNet models as baselines, and explore the impact of ensembling strategies on prediction accuracy. With this combination of our dataset and baseline models drawn from the literature we aim to provide a standardized and challenging benchmark for evaluating machine learning approaches to solid mechanics. Our results highlight both the promise and limitations of popular current models, and demonstrate the utility of this dataset as a testbed for advancing machine learning in fracture mechanics research.

Benchmark dataset↗
DOE OSTI2025

Projection-based multifidelity linear regression for data-scarce applications

Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to acquire. This work develops multifidelity methods for multiple-input multiple-output linear regression targeting data-limited applications with high-dimensional outputs. Multifidelity methods integrate many inexpensive low-fidelity model evaluations with limited, costly high-fidelity evaluations. We introduce two projection-based multifidelity linear regression approaches with linear and nonlinear features that leverage principal component basis vectors for dimensionality reduction and combine multifidelity data through: (i) a direct data augmentation using low-fidelity data, and (ii) a data augmentation incorporating explicit linear corrections between low-fidelity and high-fidelity data. The data augmentation approaches combine high-fidelity and low-fidelity data into a unified training set and train the linear regression model through weighted least squares with fidelity-specific weights. We introduce a proximity-based weighting scheme with automatic weight selection strategy through cross-validation. Here, the proposed multifidelity linear regression methods are demonstrated on approximating the surface pressure field of a hypersonic vehicle in flight and the temperature field on an aircraft disc braking system. In an ultra low-data regime of no more than twelve high-fidelity samples, multifidelity linear regression achieves approximately 2% – 12% improvement in median accuracy and a higher R 2 score relative to single-fidelity methods at comparable computational cost.

data augmentation↗
DOE OSTI2022

Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow

We report, in fluid physics, data-driven models to enhance or accelerate time to solution are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for Navier–Stokes equations to approximate spatiotemporal fluid dynamics in the canonical case of isotropic turbulence in a triply periodic box. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous neural ordinary differential equations for tracking the evolution of the governing differential equation. We demonstrate this technique on a three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Weighted greedy-optimal design of computer experiments for kernel-based and Gaussian process model emulation and calibration

This article is concerned with the approximation of high-dimensional functions by kernel-based methods. Motivated by uncertainty quantification, which often necessitates the construction of approximations that are accurate with respect to a probability density function of random variables, we aim at minimizing the approximation error with respect to a weighted $L^p$-norm. We present a greedy procedure for designing computer experiments based upon a weighted modification of the pivoted Cholesky factorization. The method successively generates nested samples with the goal of minimizing error in regions of high probability. Numerical experiments validate that this new importance sampling strategy is superior to other sampling approaches, especially when used with non-product probability density functions. We also show how to use the proposed algorithm to efficiently generate surrogates for inferring unknown model parameters from data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Estimation of Respirable Aerosol Release Fractions through Stress Corrosion Crack-Like Geometries

The formation of a stress corrosion crack (SCC) in the canister wall of a dry cask storage system (DCSS) has been identified as a potential issue for the long-term storage of spent nuclear fuel. The presence of an SCC in a storage system could represent a through-wall flow path from the canister interior to the environment. Modern, vertical DCSSs are of particular interest due to the significant backfill pressurization of the canister, up to approximately 800 kPa. This pressure differential offers a relatively high driving potential for blowdown of any particulates that might be present in the canister. In this study, the carrier gas flow rates and aerosol transmission properties were evaluated for an engineered microchannel with characteristic dimensions similar to those of an SCC. The microchannel was formed by mating two gage blocks with a slot orifice measuring 28.9 μm (0.0011 in.) tall by 12.7 mm (0.500 in.) wide by 8.86 mm (0.349 in.) long (flow length). Surrogate aerosols of cerium oxide, Ce0 2 , were seeded and mixed inside a pressurized tank. The aerosol characteristics were measured immediately upstream and downstream of the simulated SCC at elevated and ambient pressures, respectively. These data sets are intended to demonstrate a new capability to characterize SCCs under well-controlled boundary conditions. Separate modeling efforts are also underway that will be validated using these data. The test apparatus and procedures developed in this study can be easily modified for the evaluation of more complex SCC-like geometries including laboratory-grown SCC samples.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion

Abstract. Rivers and river habitats around the world are under sustained pressure from human activities and the changing global environment. Our ability to quantify and manage the river states in a timely manner is critical for protecting the public safety and natural resources. In recent years, vector-based river network models have enabled modeling of large river basins at increasingly fine resolutions, but are computationally demanding. This work presents a multistage, physics-guided, graph neural network (GNN) approach for basin-scale river network learning and streamflow forecasting. During training, we train a GNN model to approximate outputs of a high-resolution vector-based river network model; we then fine-tune the pretrained GNN model with streamflow observations. We further apply a graph-based, data-fusion step to correct prediction biases. The GNN-based framework is first demonstrated over a snow-dominated watershed in the western United States. A series of experiments are performed to test different training and imputation strategies. Results show that the trained GNN model can effectively serve as a surrogate of the process-based model with high accuracy, with median Kling–Gupta efficiency (KGE) greater than 0.97. Application of the graph-based data fusion further reduces mismatch between the GNN model and observations, with as much as 50 % KGE improvement over some cross-validation gages. To improve scalability, a graph-coarsening procedure is introduced and is demonstrated over a much larger basin. Results show that graph coarsening achieves comparable prediction skills at only a fraction of training cost, thus providing important insights into the degree of physical realism needed for developing large-scale GNN-based river network models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Experimental Measurements of Soot and Soot Precursors of Sustainable Aviation Fuels

Sustainable Aviation Fuels (SAFs) are promising for decarbonizing the challenging-to-electrify aerospace sector and reducing yearly greenhouse gas emissions by thousands of tons. Developing predictive detailed chemical kinetic models for SAF (and traditional) fuel mixtures necessitates a robust experimental database to elucidate the intricate pyrolysis and oxidation chemistry of SAF fuel blend components. Two critical components of SAF surrogate mixtures are iso-dodecane isomers and trans-decalin. This report characterizes the structure of three slightly sooting, laminar, non-premixed, nitrogen-diluted Planar Mixing Layer Flames (PMLFs) fueled by either pure ethylene, ethylene doped with 1500ppm of either 2,2,4,6,6-pentamethyl-heptane or trans-decalin. The flames have the same stoichiometric mixture fraction and total mole fraction of fuels in the fuel stream (X F,F = X C2H4,F + X Dopant,F = 0.26), resulting in approximately the same maximum temperature (T max ≈ 1800 K) to highlight the effect of doping on soot and its gaseous precursors. The PMLF is unconfined and established between adjacent planar jets of the fuel and oxidizer streams surrounded by annular shielding nitrogen. The flow is stabilized by exhausting the hot buoyant flame products through a slot in a downstream plate onto which the cold flow impinges. Importantly, any horizontal cross-section of the PMLF has a self-similar thermochemical structure of a reactive boundary layer that grows at increasing Height Above the Burner (HAB). Therefore, the horizontal cross-sections of a PMLF can be modeled as a One-Dimensional Counterflow Flame (1D-CF) with very low strain rates which are unachievable in traditional CFs and yield a structure that is several millimeters thick. In brief, the PMLF is ideal for performing spatially resolved measurements with minimal effects of the sampling-induced perturbations, to develop and validate detailed chemical kinetic models on time scales of a few tens of milliseconds that are relevant in aviation applications. The horizontal profiles of C0-C18 gaseous species at HAB = 50 mm and soot volume fraction at 25 and 50 mm are measured using capillary sampling followed by GC-MS analyses and Laser Induced Emission Spectroscopy (LIES), respectively. Additionally, the Elastic Laser Light Scattering (E-LLS) coefficient is measured at HAB = 50 mm and 25 mm to determine the strain rates of the 1D-CFs equivalent to the characterized PMLF horizontal cross sections and the profile of the E-LLS equivalent diameter of soot. The comparison of the results in the base PMLF fueled only by ethylene with those in either doped PMLF in which 1500 ppm of ethylene are replaced with 2,2,4,6,6-pentamethyl-heptane or trans-decalin, reveals that the doping increases the mole fraction of several Polycyclic Aromatic Hydrocarbons (PAHs) at both HABs by a factor of approximately 2.5, and the soot volume fraction at both HABs by factors of ~1.5 for 2,2,4,6,6-pentamethyl-heptane and ~1.25 for trans-decalin doping, respectively. Conversely, doping causes a near doubling of the soot equivalent diameter only at HAB = 50 mm, without significant effects at 25 mm. The experimental results partially validate the tested state-of-the-art detailed chemical kinetic model and also point toward the further improvement of its predictive capabilities.

10 SYNTHETIC FUELS↗
DOE OSTI2023

Improving Estimation of the Koopman Operator with Kolmogorov–Smirnov Indicator Functions

It has become common to perform kinetic analysis using approximate Koopman operators that transform high-dimensional timeseries of observables into ranked dynamical modes. The key to the practical success of the approach is the identification of a set of observables that form a good basis on which to expand the slow relaxation modes. Good observables are, however, difficult to identify a priori and suboptimal choices can lead to significant underestimations of characteristic time scales. Leveraging the representation of slow dynamics in terms of Hidden Markov Models (HMM), we propose a simple and computationally efficient clustering procedure to infer surrogate observables that form a good basis for slow modes. Here, we apply the approach to an analytically solvable model system as well as on three protein systems of different complexities. We consistently demonstrate that the inferred indicator functions can significantly improve the estimation of the leading eigenvalues of Koopman operators and correctly identify key states and transition time scales of stochastic systems, even when good observables are not known a priori.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction

In this paper, we propose a variational approach to estimate eddy viscosity using forward sensitivity method (FSM) for closure modeling in nonlinear reduced order models. FSM is a data assimilation technique that blends model's predictions with noisy observations to correct initial state and/or model parameters. We apply this approach on a projection based reduced order model (ROM) of the one-dimensional viscous Burgers equation with a square wave defining a moving shock, and the two-dimensional vorticity transport equation formulating a decay of Kraichnan turbulence. We investigate the capability of the approach to approximate an optimal value for eddy viscosity with different measurement configurations. Specifically, we show that our approach can sufficiently assimilate information either through full field or sparse noisy measurements to estimate eddy viscosity closure to cure standard Galerkin reduced order model (GROM) predictions. Therefore, our approach provides a modular framework to correct forecasting error from a sparse observational network on a latent space. We highlight that the proposed GROM-FSM framework is promising for emerging digital twin applications, where real-time sensor measurements can be used to update and optimize surrogate model's parameters.

42 ENGINEERING↗
DOE OSTI2020

sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications

Bayesian optimization (BO) is an effective surrogate-based method that has been widely used to optimize simulation-based applications. While the traditional Bayesian optimization approach only applies to single-fidelity models, many realistic applications provide multiple levels of fidelity with various levels of computational complexity and predictive capability. In this work, we propose a multi-fidelity Bayesian optimization method for design applications with both known and unknown constraints. The proposed framework, called sMF-BO-2CoGP, is built on a multi-level CoKriging method to predict the objective function. An external binary classifier, which we approximate using a separate CoKriging model, is used to distinguish between feasible and infeasible regions. Finally, the sMF-BO-2CoGP method is demonstrated using a series of analytical examples and a flip-chip application for design optimization to minimize the deformation due to warping under thermal loading conditions.

42 ENGINEERING↗
DOE OSTI2024

Optimization of a High Pressure Turbine Blade and Sector-Based Annular Rig Design for Supercritical CO2 Power Cycle Representative Testing

Abstract As part of the ongoing research into the design of hardware for zero emission cycles, a first-stage high-pressure turbine (HPT) blade is optimized for a 300 MWe supercritical CO2 (sCO2) power cycle using the surrogate-assisted genetic algorithm optimizer in Numeca FINE/Design three-dimensional with objectives of increasing efficiency and decreasing heat load to the blade. Supercritical CO2 property tables are constructed from NIST REFPROP data for the condensable gas simulation in FINE/Turbo. A detailed mesh sensitivity study is performed for a baseline design to identify the proper-grid refinement and efficiently allocate resources for the optimization. Seventy design variables are selected for the initial population generation. Self-organizing maps are then used to focus the design variables on the most important ones affecting the objective functions. The optimization results in approximately 3000 three-dimensional Reynolds Averaged Navier Stokes simulations of different blade shapes with increases in efficiency of up to 0.85% and decreases in heat load of 14%. Families of blade shapes are identified for experimental testing in an annular rig at the Purdue Experimental Turbine Aerothermal Laboratory. A design to adapt the annular cascade for testing optimized geometries is introduced, which features eccentric radius sectors allowing for scaled-up geometries of sCO2 optimized blade profiles to be tested at design cycle representative conditions at high Reynolds numbers in dry air. Analysis into the effects of Reynolds number, working fluid, and geometric relations are presented to prove the efficacy of the test method.

Engineering↗
DOE OSTI2020

Mechanistic and predictive understanding of needle litter decay in semi-arid mountain ecosystems experiencing unprecedented vegetation mortality. Final report

This research compared needle litter decay rates and underlying mechanisms between litter released from insect-killed trees to that of naturally senesced litter from healthy trees across climate variables with elevation and snowmelt rates as variables. Our research was designed to address questions of whether there is a significant impact to C and N cycling at the terrestrial-atmospheric interface in association with pine needle decomposition and the role of changes in soil moisture and temperature as a function of these processes. Field activities were primarily conducted within the Rocky Mountain Biological Laboratory’s (RMBL) East River watershed across a spectrum of ecoregions that spanned approximately 700 vertical meters. Forested catchments at these elevations in southwestern Colorado are threatened by heat-induced drought, altered snowmelt patterns, and invasive insect infestation that have all been associated with climate change. Needle litter from two members of the Pinaceae family experiencing large-scale insect infestations (spruce and lodgepole pine) were collected, redeployed and then sampled over the course of three years. We experimentally tested three tractable parameters that are known to impact litter decay and biogeochemical cycling and are relevant to bark beetle-induced tree mortality: (1) moisture content, (2) temperature by using elevation as a surrogate, and (3) changes to litter compositional chemistry that result from beetle infestation. We examined decay processes and monitored the migration of soluble and gaseous nitrogen and carbon species as well as microbiological signatures of decay such as biodegradable fractions, metal availability and the temporal succession of associated microorganisms at the terrestrial interface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments

Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational autoencoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific simulations. However, they have several drawbacks, including training instability and inability to cover the entire data distribution, especially for regions where data are rare. This is particularly challenging for whole-event, full-detector simulations in high-energy heavy-ion experiments, such as sPHENIX at the Relativistic Heavy Ion Collider and Large Hadron Collider experiments, where thousands of particles are produced per event and interact with the detector. This work investigates the effectiveness of denoising diffusion probabilistic models (DDPMs) as an AI-based generative surrogate model for the sPHENIX experiment that includes the heavy-ion event generation and response of the entire calorimeter stack. DDPM performance in sPHENIX simulation data is compared with a popular rival, GANs. Results show that both DDPMs and GANs can reproduce the data distribution where the examples are abundant (low-to-medium calorimeter energies). Nonetheless, DDPMs significantly outperform GANs, especially in high-energy regions where data are rare. Additionally, DDPMs exhibit superior stability compared to GANs. The results are consistent between both central and peripheral centrality heavy-ion collision events. Moreover, DDPMs offer a substantial speedup of approximately a factor of 100 compared to the traditional Geant4 simulation method.

42 ENGINEERING↗
DOE OSTI2024

Phonon modal analysis of thermal transport in ThO 2 with point defects using equilibrium molecular dynamics

Defects can significantly degrade the thermal conductivity of ThO 2 , an advanced nuclear fuel material as well as a surrogate for other fluorite-structured materials. Here, we investigate how point defects in ThO 2 impact phonon mode-resolved thermal transport. By incorporating phonon modes from lattice dynamics, we decompose the trajectory and heat flux to phonon normal mode space and extract key phonon properties, including phonon relaxation times and their contributions to thermal conductivity. We implement two methods. The first method is based on the Green Kubo formalism to resolve the contribution of each phonon mode to thermal conductivity. The second resolves the lifetime of individual phonon modes and the thermal conductivity is calculated using the Boltzmann transport equation within relaxation time approximation. Notably, a lower contribution of acoustic modes is revealed compared to perturbative approaches considering only three-phonon scattering processes. The effects of four types of point defects are evaluated. The strongest impact on a reduction in thermal conductivity is from Th interstitials, followed by Th vacancies. O interstitials/vacancies have a similar impact, albeit smaller than defects on the thorium sublattice. These observations are consistent with previous studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Review of ECCS Acceptance Criteria and Experimental Basis Evolution Toward Fuel Fragmentation, Relocation, and Dispersal Studies

The U.S. nuclear industry is pursuing extensions of light water reactor (LWR) fuel burnup and enrichment limits to approximately 75 GWd/t and 10 wt.% 235 U to achieve economic and operational benefits. A central safety consideration in this effort is the behavior of high burnup (HBu) fuel during loss-of-coolant accidents (LOCAs), particularly fuel fragmentation, relocation, and dispersal (FFRD). Here, this work provides a historical and technical review of U.S. LOCA regulation and experimentation, clarifying how the evolution of Emergency Core Cooling System (ECCS) acceptance criteria in 10 CFR 50.46 has shaped both testing approaches and interpretations of fuel safety. The study revisits the original intent of the ECCS criteria, showing that the peak cladding temperature and equivalent cladding reacted limits were developed as surrogates to preserve a coolable geometry. The explicit inclusion of the coolable geometry criterion in the regulation was intended to emphasize the underlying safety philosophy and as a safeguard against unforeseen failure modes, an intent that remains directly relevant to modern concerns regarding FFRD. The review traces the lineage of HBu LOCA experiments to the Argonne National Laboratory furnace tests, from which subsequent programs at Studsvik, Halden, and Oak Ridge National Laboratory were derived. These tests employed a 5 °C/s heating rate inherited from early embrittlement studies, a stylized temperature history that does not represent actual LWR LOCA thermal-hydraulics. Comparison of these test conditions to pressurized water reactor large break LOCAs and separate effects data indicates that the existing HBu LOCA database may not be fully applicable to all LWR LOCA scenarios, from which a qualitative framework for applicability is proposed.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

On the Use of Satellite Nightlights for Power Outages Prediction

Hurricanes are a dominant disaster in the Caribbean, always causing serious power outages throughout the islands. Hurricane Maria was a prime example, causing unimaginable destruction of the power infrastructure of Puerto Rico (PR). Consequently, one month after the hurricane landfall, approximately 80% of the population was still without power. After an event of such massive destruction, the electric power restoration process progresses very slowly. This timeline can be improved using power outage (PO) forecast models that help identify the vulnerable places before the hurricane landfall. Generally, these models are trained with historical power outages records, associated data on weather conditions, and additional information about the natural and built environments. However, PO records are often difficult to acquire, and, in many instances, the power utility companies may not record them. This study utilizes a satellite-based Visible Infrared Imaging Radiometer Suite (VIIRS) night light data product as a surrogate for the power delivery to predict hurricane-induced PO in areas having limited to nonexistent historical data records. The processed satellite data is then used along with geographic variables, and simulated weather data to formulate machine learning-based algorithms to predict PO for future hurricane events. These models are applied and validated in the context of the PR catastrophic storm, Hurricane Maria.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Surrogate-Based Asynchronous Decomposition Technique for Realistic Security-Constrained Optimal Power Flow Problems

Here we present a decomposition approach for obtaining good feasible solutions for the security-constrained, alternating-current, optimal power flow (SC-AC-OPF) problem at an industrial scale and under real-world time and computational limits. The approach was designed while preparing and participating in ARPA-E’s Grid Optimization Competition (GOC) Challenge 1. The challenge focused on a near-real-time version of the SC-AC-OPF problem, where a base operating point is optimized, taking into account possible single-element contingencies, after which the system adapts its operating point following the response of automatic frequency droop controllers and voltage regulators. Our solution approach for this problem relies on state-of-the-art nonlinear programming algorithms, and it employs nonconvex relaxations for complementarity constraints, a specialized two-stage decomposition technique with sparse approximations of recourse terms and contingency ranking and prescreening. The paper describes and justifies our approach and outlines the features of its implementation, including functions and derivatives evaluation, warm-starting strategies, and asynchronous parallelism. We discuss the results of the independent benchmark of our approach by ARPA-E’s GOC team in Challenge 1, where it was found to consistently produce high-quality solutions across a wide range of network sizes and difficulty, and conclude by outlining future extensions of the approach.

97 MATHEMATICS AND COMPUTING↗