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DOE OSTI2025

Accuracy Guarantees and Quantum Advantage in Analog Open Quantum Simulation with and without Noise

Many-body open quantum systems, described by Lindbladian master equations, are a rich class of physical models that display complex equilibrium and out-of-equilibrium phenomena which remain to be understood. In this paper, we theoretically analyze noisy analog quantum simulation of geometrically local open quantum systems and provide evidence that this problem both is hard to simulate on classical computers and could be approximately solved on near-term quantum devices. First, given a noiseless quantum simulator, we show that the dynamics of local observables and the fixed-point expectation values of rapidly mixing local observables in geometrically local Lindbladians can be obtained to a precision of ϵ in time that is poly ( ϵ − 1 ) and uniform in system size. Furthermore, we establish that the quantum simulator would provide a superpolynomial advantage, in run-time scaling with respect to the target precision and either the evolution time (when simulating dynamics) or the Lindbladian’s decay rate (when simulating fixed points), over any classical algorithm for these problems, assuming BQP ≠ BPP . We then consider the presence of noise in the quantum simulator in the form of additional geometrically local Lindbladian terms. We show that the simulation tasks considered in this paper are stable to errors; i.e., they can be solved to a noise-limited, but system-size independent, precision. Finally, we establish that, assuming BQP ≠ BPP , there are stable geometrically local Lindbladian simulation problems such that, as the noise rate on the simulator is reduced, classical algorithms must take time superpolynomially longer in the inverse noise rate to attain the same precision as the analog quantum simulator. Published by the American Physical Society 2025

Kashyap, Vikram (ORCID:0000000208195207)↗

DOE OSTI2013

Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.

Simmhan, Yogesh↗

DOE OSTI2023

Progressive Tree-Based Compression of Large-Scale Particle Data

Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. In conclusion, the improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

MO-C-17A-03: A GPU-Based Method for Validating Deformable Image Registration in Head and Neck Radiotherapy Using Biomechanical Modeling

Purpose: Deformable image registration (DIR) plays a pivotal role in head and neck adaptive radiotherapy but a systematic validation of DIR algorithms has been limited by a lack of quantitative high-resolution groundtruth. We address this limitation by developing a GPU-based framework that provides a systematic DIR validation by generating (a) model-guided synthetic CTs representing posture and physiological changes, and (b) model-guided landmark-based validation. Method: The GPU-based framework was developed to generate massive mass-spring biomechanical models from patient simulation CTs and contoured structures. The biomechanical model represented soft tissue deformations for known rigid skeletal motion. Posture changes were simulated by articulating skeletal anatomy, which subsequently applied elastic corrective forces upon the soft tissue. Physiological changes such as tumor regression and weight loss were simulated in a biomechanically precise manner. Synthetic CT data was then generated from the deformed anatomy. The initial and final positions for one hundred randomly-chosen mass elements inside each of the internal contoured structures were recorded as ground truth data. The process was automated to create 45 synthetic CT datasets for a given patient CT. For instance, the head rotation was varied between +/− 4 degrees along each axis, and tumor volumes were systematically reduced up to 30%. Finally, the original CT and deformed synthetic CT were registered using an optical flow based DIR. Results: Each synthetic data creation took approximately 28 seconds of computation time. The number of landmarks per data set varied between two and three thousand. The validation method is able to perform sub-voxel analysis of the DIR, and report the results by structure, giving a much more in depth investigation of the error. Conclusions: We presented a GPU based high-resolution biomechanical head and neck model to validate DIR algorithms by generating CT equivalent 3D volumes with simulated posture changes and physiological regression.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

LiMnO2 polymorphs

Energies of LiMnO2 polymorphs computed within various DFT approximations

36 MATERIALS SCIENCE↗

DOE OSTI2022

GLUE Code: A framework handling communication and interfaces between scales

Many scientific applications are inherently multiscale in nature. Such complex physical phenomena often require simultaneous execution and coordination of simulations spanning multiple time and length scales. This is possible by combining expensive small-scale simulations (such as molecular dynamics simulations) with larger scale simulations (such continuum limit/hydro solvers) to allow for considerably larger systems using task and data parallelism. However, the granularity of the tasks can be very large and often leads to load imbalance. Traditionally, we use approximations to streamline the computation of the more costly interactions and this introduces trade-offs between simulation cost and accuracy. In recent years, the available computational power and the advances in machine learning have made computing these scale-bridging interactions and multiscale simulations more feasible. One driving application has been plasma modeling in inertial confinement fusion (ICF), which is fundamentally multiscale in nature. This requires deep understanding of how to extrapolate microscopic information into macroscopically relevant scales. For example, in ICF one needs an accurate understanding of the connection between experimental observables and the underlying microphysics. The properties of the larger scales are often affected by the microscale behavior incorporated usually into the equations of state and ionic and electronic transport coefficients (Liboff, 1959; Rinderknecht et al., 2014; Rosenberg et al., 2015; Ross et al., 2017). Instead of incorporating this information using reliable molecular dynamics (MD) simulations, one often needs to use theoretical models, due to the inability of MD to reach engineering scales (Glosli et al., 2007; Marinak et al., 1998). One approach to resolve this issue is by coupling two MD simulations of different scales via force interpolation, e.g., the AdResS method (Krekeler et al., 2018; Nagarajan et al., 2013). Another approach, which we will pursue in the scope of this work, is by enabling scale bridging between MD simulations and meso/macro-scale models through the development and support of application programming interfaces that these different applications can interact through.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Adaptive multi-index collocation for uncertainty quantification and sensitivity analysis

In this paper, we present an adaptive algorithm to construct response surface approximations of high-fidelity models using a hierarchy of lower fidelity models. Our algorithm is based on multiindex stochastic collocation and automatically balances physical discretization error and response surface error to construct an approximation of model outputs. This surrogate can be used for uncertainty quantification (UQ) and sensitivity analysis (SA) at a fraction of the cost of a purely high-fidelity approach. We demonstrate the effectiveness of our algorithm on a canonical test problem from the UQ literature and a complex multi-physics model that simulates the performance of an integrated nozzle for an unmanned aerospace vehicle. We find that when the input-output response is sufficiently smooth our algorithm produces approximations that can be up to orders of magnitude more accurate than single fidelity approximations for a fixed computational budget.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Phase-curve Pollution of Exoplanet Transit Depths

The next generation of space telescopes will enable transformative science to understand the nature and origin of exoplanets. In particular, transit spectroscopy will reveal the chemical composition of the exoplanet atmospheres with unprecedented detail thanks to precise measurements of the visible-to-infrared transit depths down to 10 parts per million. Such a level of instrumental precision raises the challenge to obtain even more precise astrophysical models so as not to significantly influence the interpretation of the observed data. We must therefore critically revisit some of the commonly accepted assumptions that were adequate for analyzing past and current observations. A common approximation in the analysis of exoplanetary primary transits is that the planet does not contribute to the recorded flux, so-called dark planet hypothesis. In this paper, we investigate the impact of the dark planet hypothesis on the parameters obtained from the analysis of transits with particular attention to the transit depth. We develop mathematical formulae and release new software to estimate the magnitude of the potential bias. These tools will be useful in the preparation of observing proposals, as well as within the scientific consortia of the James Webb Space Telescope (JWST) and the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) missions. We probe the accuracy of the mathematical formulae through the analysis of synthetic observations with the JWST Mid-InfraRed Instrument. We find that self-blending from nightside emission attenuates the transit depth by >3σ for some of the known exoplanet systems, in agreement with previous work. An additional unreported effect caused by the nightside rotating into view can also impart a significant effect, but in the opposite direction (increasing the transit depth); this effect can largely be removed with conventional detrending practices, at the expense of a slight increase in noise, and mixing astrophysical variations and instrumental drifts.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Constraints on the Emission of Gamma-Rays from M31 with HAWC

Cosmic rays, along with stellar radiation and magnetic fields, are known to make up a significant fraction of the energy density of galaxies such as the Milky Way. When cosmic rays interact in the interstellar medium, they produce gamma-ray emission which provides an important indication of how the cosmic rays propagate. Gamma rays from the Andromeda Galaxy (M31), located 785 kpc away, provide a unique opportunity to study cosmic-ray acceleration and diffusion in a galaxy with a structure and evolution very similar to the Milky Way. Using 33 months of data from the High Altitude Water Cherenkov Observatory, we search for TeV gamma rays from the galactic plane of M31. We also investigate past and present evidence of galactic activity in M31 by searching for Fermi Bubble-like structures above and below the galactic nucleus. No significant gamma-ray emission is observed, so we use the null result to compute upper limits on the energy density of cosmic rays $>10$ TeV in M31. In conclusion, the computed upper limits are approximately ten times higher than expected from the extrapolation of the Fermi LAT results.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Threshold progressions in covering and packing contexts

Here, using standard methods (due to Janson, Stein–Chen, and Talagrand) from probabilistic combinatorics, we explore the following general theme: As one progresses from each member of a family of objects $\mathcal{A}$ being “covered” by at most one object in a random collection $\mathcal{C}$, to being covered at most λ times, to being covered at least once, to being covered at least λ times, a hierarchy of thresholds emerge. We will use examples from extremal set theory, combinatorics, and additive number theory to see how these results vary according to the context, and level of dependence introduced.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Optimization of Depletion Modeling and Simulation for the High Flux Isotope Reactor

Monte Carlo based depletion tools used for the high-fidelity modeling and simulation of the High Flux Isotope Reactor (HFIR) come at a great computational cost; finding sufficient approximations is necessary to make the use of these tools feasible. The optimization of the neutronics and depletion model for the HFIR is based on two factors: (i) the explicit representation of the involute fuel plates with sets of polyhedra and (ii) the treatment of depletion mixtures and control element position during depletion calculations. A very fine representation (i.e., more polyhedra in the involute plate approximation) does not significantly improve simulation accuracy. The recommended representation closely represents the physical plates and ensures sufficient fidelity in regions with high flux gradients. Including the fissile targets in the central flux trap of the reactor as depletion mixtures has the greatest effect on the calculated cycle length, while localized effects (e.g., the burnup of specific isotopes or the power distribution evolution over the cycle) are more noticeable consequences of including a critical control element search or depleting burnable absorbers outside the fuel region.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2018

Parameter distribution mapping in a gas turbine engine

A profile map of parameter values in a region is determined based on average values along linear paths through the region. In one example, a temperature map of a region of a gas turbine engine is created based on time-of-flight measurements from acoustic transceiver pairs arranged circumferentially around the region. A speed of sound for each transceiver pair is determined based on the time-of-flight measurements. An average temperature along each path is estimated from the time of flight, and the profile map is computed from the average temperatures. The profile map may be computed using a polynomial approximation technique, a grid optimization technique or a basis function technique.

42 ENGINEERING↗

DOE OSTI2016

Impact of Spatial Discretization on Reactivity Biases in Depleted TRIGA Fuel

Ongoing work at Kansas State University (KSU) and the University of Wisconsin involves the determination of fuel compositions in TRIGA reactors. Modeling of depletion requires a number of approximations to be made, which often includes selection of an appropriate spatial discretization for tracking material evolution in time. Incorporation of temperature feedback also requires such spatial zoning. Any approximation made leads to computational biases, i.e., differences between computed and accurate values of some parameter of interest. The objective of this study is to understand the potential magnitude of biases introduced by the selected spatial discretization. Specifically, biases in the reactivity of a square-pitch unit cell representative of a typical TRIGA fuel element were studied as a function of burnup for different radial and axial discretizations. Temperature effects were also studied by introducing a fixed, radial temperature profile. The results indicate a very fine radial discretization (> 10 radial regions) may be needed to eliminate biases (of statistical significance) from temperature feedback effects, while an axial discretization of {approx_equal}7 regions may be sufficient to eliminate biases due to evolution of the fuel composition. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Bias-Variance Trade-Off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs

Physics-Informed Neural Networks (PINNs) have triggered a paradigm shift in scientific computing, leveraging mesh-free properties and robust approximation capabilities. While proving effective for low-dimensional partial differential equations (PDEs), the computational cost of PINNs remains a hurdle in high-dimensional scenarios. This is particularly pronounced when computing high-order and high-dimensional derivatives in the physics-informed loss. Randomized Smoothing PINN (RS-PINN) introduces Gaussian noise for stochastic smoothing of the original neural net model, enabling the use of Monte Carlo methods for derivative approximation, which eliminates the need for costly automatic differentiation. Despite its computational efficiency, especially in the approximation of high-dimensional derivatives, RS-PINN introduces biases in both loss and gradients, negatively impacting convergence, especially when coupled with stochastic gradient descent (SGD) algorithms. We present a comprehensive analysis of biases in RS-PINN, attributing them to the nonlinearity of the Mean Squared Error (MSE) loss as well as the intrinsic nonlinearity of the PDE itself. We propose tailored bias correction techniques, delineating their application based on the order of PDE nonlinearity. The derivation of an unbiased RS-PINN allows for a detailed examination of its advantages and disadvantages compared to the biased version. Specifically, the biased version has a lower variance and runs faster than the unbiased version, but it is less accurate due to the bias. To optimize the bias-variance trade-off, we combine the two approaches in a hybrid method that balances the rapid convergence of the biased version with the high accuracy of the unbiased version. In addition to methodological contributions, we present an enhanced implementation of RS-PINN. Extensive experiments on diverse high-dimensional PDEs, including Fokker-Planck, Hamilton-Jacobi-Bellman (HJB), viscous Burgers’, Allen-Cahn, and Sine-Gordon equations, illustrate the bias-variance trade-off and highlight the effectiveness of the hybrid RS-PINN. Empirical guidelines are provided for selecting biased, unbiased, or hybrid versions, depending on the dimensionality and nonlinearity of the specific PDE problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Gyro-fluid and two-fluid theory and simulations of edge-localized-modes

This paper reports on the theoretical and simulation results of a gyro-Landau-fluid extension of the BOUT++ code, which contributes to increasing the physics understanding of edge-localized-modes (ELMs). Large ELMs with low-to-intermediate-n peeling-ballooning (P-B) modes are significantly suppressed due to finite Larmor radius (FLR) effects when the ion temperature increases. For type-I ELMs, it is found from linear simulations that retaining complete first order FLR corrections as resulting from the incomplete “gyroviscous cancellation” in Braginskii's two-fluid model is necessary to obtain good agreement with gyro-fluid results for high ion temperature cases (T{sub i}≽3 keV) when the ion density has a strong radial variation, which goes beyond the simple local model of ion diamagnetic stabilization of ideal ballooning modes. The maximum growth rate is inversely proportional to T{sub i} because the FLR effect is proportional to T{sub i}. The FLR effect is also proportional to toroidal mode number n, so for high n cases, the P-B mode is stabilized by FLR effects. Nonlinear gyro-fluid simulations show results that are similar to those from the two-fluid model, namely that the P-B modes trigger magnetic reconnection, which drives the collapse of the pedestal pressure. Due to the additional FLR-corrected nonlinear E × B convection of the ion gyro-center density, for a ballooning-dominated equilibrium the gyro-fluid model further limits the radial spreading of ELMs. In six-field two fluid simulations, the parallel thermal diffusivity is found to prevent the ELM encroachment further into core plasmas and therefore leads to steady state L-mode profiles. The simulation results show that most energy is lost via ion channel during an ELM event, followed by particle loss and electron energy loss. Because edge plasmas have significant spatial inhomogeneities and complicated boundary conditions, we have developed a fast non-Fourier method for the computation of Landau-fluid closure terms based on an accurate and tunable approximation. The accuracy and the fast computational scaling of the method have been demonstrated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2016

An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations

In this paper, we develop an improved multilevel Monte Carlo (MLMC) method for estimating cumulative distribution functions (CDFs) of a quantity of interest, coming from numerical approximation of large-scale stochastic subsurface simulations. Compared with Monte Carlo (MC) methods, that require a significantly large number of high-fidelity model executions to achieve a prescribed accuracy when computing statistical expectations, MLMC methods were originally proposed to significantly reduce the computational cost with the use of multifidelity approximations. The improved performance of the MLMC methods depends strongly on the decay of the variance of the integrand as the level increases. However, the main challenge in estimating CDFs is that the integrand is a discontinuous indicator function whose variance decays slowly. To address this difficult task, we approximate the integrand using a smoothing function that accelerates the decay of the variance. In addition, we design a novel a posteriori optimization strategy to calibrate the smoothing function, so as to balance the computational gain and the approximation error. The combined proposed techniques are integrated into a very general and practical algorithm that can be applied to a wide range of subsurface problems for high-dimensional uncertainty quantification, such as a fine-grid oil reservoir model considered in this effort. The numerical results reveal that with the use of the calibrated smoothing function, the improved MLMC technique significantly reduces the computational complexity compared to the standard MC approach. Finally, we discuss several factors that affect the performance of the MLMC method and provide guidance for effective and efficient usage in practice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

A Perspective on Quantum Computing Applications in Quantum Chemistry Using 25-100 Logical Qubits

The intersection of quantum computing and quantum chemistry represents a promising frontier for achieving quantum utility in domains of both scientific and societal relevance. Owing to the exponential growth of classical resource requirements for simulating quantum systems, quantum chemistry has long been recognized as a natural candidate for quantum computation. This perspective focuses on identifying scientifically meaningful use cases where early fault-tolerant quantum computers, which are considered to be equipped with approximately 25-100 logical qubits, could deliver tangible impact. While recent advances in classical computing have pushed the boundaries of tractable simulations to unprecedented scales, this logical-qubit regime represents the first window where quantum devices can pursue qualitatively distinct strategies, such as polynomial-scaling phase estimation, direct simulation of quantum dynamics, and active-space embedding, that remain challenging for classical solvers, such as multireference charge-transfer and conical-intersection states central to photochemistry and materials design. We highlight near-term opportunities in algorithm and software design, discuss representative chemical problems suited for quantum acceleration, and propose strategic roadmaps and collaborative pathways for advancing practical quantum utility in quantum chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Physically regularized machine learning emulators of aerosol activation

Abstract. The activation of aerosol into cloud droplets is an important step in the formation of clouds and strongly influences the radiative budget of the Earth. Explicitly simulating aerosol activation in Earth system models is challenging due to the computational complexity required to resolve the necessary chemical and physical processes and their interactions. As such, various parameterizations have been developed to approximate these details at reduced computational cost and accuracy. Here, we explore how machine learning emulators can be used to bridge this gap in computational cost and parameterization accuracy. We evaluate a set of emulators of a detailed cloud parcel model using physically regularized machine learning regression techniques. We find that the emulators can reproduce the parcel model at higher accuracy than many existing parameterizations. Furthermore, physical regularization tends to improve emulator accuracy, most significantly when emulating very low activation fractions. This work demonstrates the value of physical constraints in machine learning model development and enables the implementation of improved hybrid physical and machine learning models of aerosol activation into next-generation Earth system models.

58 GEOSCIENCES↗