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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

IAEA Coordinated Research Project on HTGR Reactor Physics, Thermal-hydraulics and Depletion Uncertainty Analysis

The continued development of High Temperature Gas Cooled Reactors (HTGRs) requires verification of HTGR design and safety features with reliable high fidelity physics models and robust, efficient, and accurate codes. The predictive capability of coupled neutronics/thermal-hydraulics and depletion simulations for reactor design and safety analysis can be assessed with sensitivity analysis (SA) and uncertainty analysis (UA) methods. Uncertainty originates from errors in physical data, manufacturing uncertainties, modelling and computational algorithms. (The interested reader is referred to the large body of published SA and UA literature for a more complete overview of the various types of uncertainties, methodologies and results obtained). SA is helpful for ranking the various sources of uncertainty and error in the results of core analyses. SA and UA are required to address cost, safety, and licensing needs and should be applied to all aspects of reactor multi-physics simulation. SA and UA can guide experimental, modelling, and algorithm research and development. Current SA and UA rely either on derivative-based methods such as stochastic sampling methods or on generalized perturbation theory to obtain sensitivity coefficients. Neither approach addresses all needs. In order to benefit from recent advances in modelling and simulation and the availability of new covariance data (nuclear data uncertainties) extensive sensitivity and uncertainty studies are needed for quantification of the impact of different sources of uncertainties on the design and safety parameters of HTGRs. Only a parallel effort in advanced simulation and in nuclear data improvement will be able to provide designers with more robust and well validated calculation tools to meet design target accuracies. In February 2009, the Technical Working Group on Gas-Cooled Reactors (TWG-GCR) of the International Atomic Energy Agency (IAEA) recommended that the proposed Coordinated Research Program (CRP) on the HTGR Uncertainty Analysis in Modelling (UAM) be implemented. This CRP is a continuation of the previous IAEA and Organization for Economic Co-operation and Development (OECD)/Nuclear Energy Agency (NEA) international activities on Verification and Validation (V&V) of available analytical capabilities for HTGR simulation for design and safety evaluations. Within the framework of these activities different numerical and experimental benchmark problems were performed and insight was gained about specific physics phenomena and the adequacy of analysis methods.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2013

Realistic multisite lattice-gas modeling and KMC simulation of catalytic surface reactions: Kinetics and multiscale spatial behavior for CO-oxidation on metal (100) surfaces

A realistic molecular-level description of catalytic reactions on single-crystal metal surfaces can be provided by stochastic multisite lattice-gas (msLG) models. This approach has general applicability, although in this report, we will focus on the example of CO-oxidation on the unreconstructed fcc metal (100) or M(100) surfaces of common catalyst metals M = Pd, Rh, Pt and Ir (i.e., avoiding regimes where Pt and Ir reconstruct). These models can capture the thermodynamics and kinetics of adsorbed layers for the individual reactants species, such as CO/M(100) and O/M(100), as well as the interaction and reaction between different reactant species in mixed adlayers, such as (CO + O)/M(100). The msLG models allow population of any of hollow, bridge, and top sites. This enables a more flexible and realistic description of adsorption and adlayer ordering, as well as of reaction configurations and configuration-dependent barriers. Adspecies adsorption and interaction energies, as well as barriers for various processes, constitute key model input. The choice of these energies is guided by experimental observations, as well as by extensive Density Functional Theory analysis. Model behavior is assessed via Kinetic Monte Carlo (KMC) simulation. We also address the simulation challenges and theoretical ramifications associated with very rapid diffusion and local equilibration of reactant adspecies such as CO. These msLG models are applied to describe adsorption, ordering, and temperature programmed desorption (TPD) for individual CO/M(100) and O/M(100) reactant adlayers. In addition, they are also applied to predict mixed (CO + O)/M(100) adlayer structure on the nanoscale, the complete bifurcation diagram for reactive steady-states under continuous flow conditions, temperature programmed reaction (TPR) spectra, and titration reactions for the CO-oxidation reaction. Extensive and reasonably successful comparison of model predictions is made with experimental data. Furthermore, we discuss the possible transition from traditional mean-field-type bistability and reaction kinetics for lower-pressure to multistability and enhanced fluctuation effects for moderate- or higher-pressure. Behavior in the latter regime reflects a stronger influence of adspecies interactions and also lower diffusivity in the higher-coverage mixed adlayer. We also analyze mesoscale spatiotemporal behavior including the propagation of reaction diffusion fronts between bistable reactive and inactive states, and associated nucleation-mediated transitions between these states. This behavior is controlled by complex surface mass transport processes, specifically chemical diffusion in mixed reactant adlayers for which we provide a precise theoretical formulation. The msLG models together with an appropriate treatment of chemical diffusivity enable equation-free heterogeneous coupled lattice-gas (HCLG) simulations of spatiotemporal behavior. In addition, msLG + HCLG modeling can describe coverage variations across polycrystalline catalysts surfaces, pressure variations across catalyst surfaces in microreactors, and could be incorporated into a multiphysics framework to describe mass and heat transfer limitations for high-pressure catalysis. (C) 2013 Elsevier Ltd. All rights reserved.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence.

Efficiently performing predictive studies of irradiated particle-laden turbulent flows has the potential of providing significant contributions towards better understanding and optimizing, for example, concentrated solar power systems. As there are many uncertainties inherent in such flows, conducting uncertainty quantification analyses is fundamental to improve the predictive capabilities of the numerical simulations. For largescale, multi-physics problems exhibiting high-dimensional uncertainty, characterizing the stochastic solution presents a significant computational challenge as many methods require a large number of high-fidelity, forward model solves. This requirement results in the need for a possibly infeasible number of simulations when a typical converged high-fidelity simulation requires intensive computational resources. To reduce the cost of quantifying high-dimensional uncertainties, we investigate the application of a non-intrusive, bi-fidelity approximation to estimate statistics of quantities of interest associated with an irradiated particle-laden turbulent flow. This method relies on exploiting the low-rank structure of the solution to accelerate the stochastic sampling and approximation processes by means of cheaper-to-run, lower fidelity representations. The application of this bi-fidelity approximation results in accurate estimates of the QoI statistics while requiring a small number of high-fidelity model evaluations. It also enables efficient computation of sensitivity analyses which highlight that epistemic uncertainty plays an important role in the solution of irradiated, particle-laden turbulent flow.

42 ENGINEERING↗
DOE OSTI2021

Modelling dust transport in DIII-D with DTOKS-Upgrade

Comprehensive upgrades to the dust transport code Dust in TOKamaks (DTOKS) that extend the plasma-dust interaction model are presented and compared with recent measurements of dust transport in DIII-D. Simulations incorporating variation in physical properties of graphite dust with temperature and size in a stationary plasma background suggest a substantial decrease in lifetimes due principally to thermal expansion. The trajectories of 53 dust grains identified from analysis of visible camera data taken across two similar shots were used to measure the dust particle velocity distributions. Dust tracks terminated mostly at the outer divertor strike point having a mean observation time of 2.1 ± 0.4 ms. Stochastic modelling of 200 graphite dust particles in the DIII-D tokamak performed with DTOKS-U using plasma simulations generated by OEDGE found similar behaviour, with particles ablating rapidly after acquiring a positive charge in the region close to the outer strike point, creating an acute source of neutral carbon atoms. The simulated mean lifetime, 11 ± 2 ms, showed approximate agreement with experimental observation when corrected by accounting for dust visibility and ignoring the longest trajectories 1.5 ± 0.2 ms. Synthetic diagnostic data generated from coupling the results of DTOKS-U with the visualisation software Calcam offers a powerful new tool for validation of simulations and predictive calculations of dust dynamics.

Physics↗
DOE OSTI2020

Benchmark Solutions for Radiation Transport in Stochastic Media with Inhomogeneous Material Statistics

Accurately solving implicit Monte Carlo (IMC) thermal photon transport problems with mixed material cells is important in realistic applications. The production IMC package at LLNL treats mixed material cells arising from ALE remap and hydrodynamics using the same approximate model. The new Imp IMC thermal photon transport package currently under development has both a material interface reconstruction (MIR) algorithm and a Levermore-Pomraning (LP) stochastic medium algorithm for treating mixed material cells. Existing stochastic medium algorithms for treating mixed material cells in IMC lack a complete theoretical basis. The IMC LP algorithm implementation has been demonstrated to reproduce published deterministic LP solutions for the particular case of spatially homogeneous material statistics. Realistic simulations will include spatially inhomogeneous material statistics (material mean chord lengths). In a previous investigation, the LP-model for transport in binary stochastic media in rod geometry was generalized to accommodate spatially varying material chord lengths, i.e., the mixing statistics were allowed to be nonhomogeneous. Analytical solutions were obtained and used to produce a verifi cation suite for the Imp IMC Levermore-Pomraning implementation for different spatial variations of the chord lengths. However, the accuracy of the LP model when the mixing statistics are nonhomogeneous has not been assessed and leaves open the question of whether local accuracy is improved or further degraded when chord lengths are not uniform. This shortcoming is rectifi ed here by developing benchmark analytic solutions for transport in binary Markovian stochastic mixtures in rod geometry with nonhomogeneous mixing statistics, using spatially varying chord lengths considered in the previous investigation based on the LP model. Methods for sampling a nonhomogeneous Poisson process (NHPP) are first described and used to construct individual realizations of the binary mixtures in rod geometry. Analytic solutions are then obtained for the forward and backward directed fluxes on a given realization, now viewed as a deterministic medium with alternating layers of the two materials with known interface locations. Finally, material averaged scalar fluxes are obtained using these sampling schemes with spatially linear and quadratic chord lengths and used to assess the accuracy of the previously obtained LP-model results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

A hybrid data-driven and model-based approach for computationally efficient stochastic unit commitment and economic dispatch under wind and solar uncertainty

Stochastic unit commitment (UC) and economic dispatch (ED) are imperative in dealing with uncertainty in renewable forecast for power system operation and planning such that the overall expected production cost is minimized over the planning horizon. However, accurate calculation of the expected production cost requires assessment of a very large number of different scenarios of uncertain renewable resources, such as solar and wind, which is practically infeasible to simulate in real time. This article proposes a hybrid datadriven and physics-based model-predictive paradigm to efficiently solve for stochastic unit commitment and economic dispatch considering uncertainty in wind and solar power forecasts. Here, the novelty of the approach lies in decoupling the production cost estimation from the unit commitment and economic dispatch optimization problems under uncertainty without compromising on the fidelity of the solutions. A data-driven machine learning model is first developed to predict the mean optimal production cost. A physics-based inverse problem is then solved to get the stochastic UC and ED profiles from the expected cost. The presented approach considers, for the first time, solar uncertainty in UC/ED determination and enables efficient and accurate propagation of wind and solar uncertainty to estimate the statistics of the production cost. The effectiveness of the developed approach is demonstrated systematically on a stylized RTS-GMLC single-node system. The overall framework predicts the expected cost 62.5% more accurately than the existing state-of-the-art, on unforeseen days during the entire year, and yields, for the first time, the associated physically consistent UC and ED profiles. The solutions are also shown to be flexible in providing adequate daily reserves to address any statistical deviations from probabilistic power forecasts. The computational time associated with the presented method is only about 10 s compared to over 24 h needed for a conventional stochastic UC/ED determination under uncertainty on an Intel Core i9 processor with 32 GB of RAM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks

Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Risk Assessment of Carbon Sequestration into A Naturally Fractured Reservoir at Kevin Dome, Montana

In this report, we describe risk assessment work done using the National Risk Assessment Partnership (NRAP) applied to CO 2 storage at Kevin Dome, Montana. Geologic CO 2 sequestration in saline aquifers poses certain risks including CO 2 /brine leakage through wells or non-sealing faults into groundwater or to the land surface. These risks are difficult to quantify due to data availability and uncertainty. One solution is to explore the consequences of these limitations by running large numbers of numerical simulations on the primary CO2 injection reservoir, shallow reservoirs/aquifers, faults, and wells to assess leakage risks and uncertainties. However, a large number of full-physics simulations is usually too computationally expensive. The NRAP integrated assessment model (NRAP-IAM) uses reduced order models (ROMs) developed from full-physics simulations to address this issue. A powerful stochastic framework allows NRAPIAM to explore complex interactions among many uncertain variables and evaluate the likely performance of potential sequestration sites.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1983

Structural considerations for a software life cycle dynamic simulation model

This paper presents the results of a preliminary study into the prospects for simulating the software implementation and maintenance life cycle process, with the aim of producing a computerized tool for use by management and software engineering personnel in project planning, tradeoff studies involving product, environmental, situational, and technological factors, and training. The approach taken is the modular application of a 'flow of resource' concept to the systems dynamics simulation modeling technique. The software life cycle process is represented as a number of stochastic, time-varying, interacting work tasks that each achieves one of the project milestones. Each task is characterized by the item produced, the personnel applied, and the budgetary profile.

Tausworthe, R. C.↗
NASA NTRS2005

Tectonic-Climate Interactions in Action Orogenic Belts: Quantification of Dynamic Topography with SRTM data

This project was undertaken to examine the approach to steady state in collisional mountain belts. Although the primary thrust of this grant was to look at larger collisional mountain belts, such as the Himalaya, the Tien Shan, and Southern Alps, we began by looking at smaller structures represented by growing and propagating folds. Like ranges that are evolving toward a topographic steady state, these folds undergo a series of morphologic changes as they are progressively uplifted and eroded. We wanted to document the nature of these changes and to try to discern some of the underlying controls on them. We initially focused on the Wheeler Ridge anticline in southern California. Subsequently, we progressed to looking at the topographic development and the effects of differential uplift and glaciation on the Kyrgyz Range in the northern Tien Shan. This range is unusual inasmuch as it is transformed along its length from a simple uplift with a largely preserved Mesozoic erosion surface arching across it to a highly dissected and heavily glaciated uplift in the region where uplift has been sustained at higher rates over longer intervals. In efforts to understand the distribution of erosion rates at 10(exp 3) - 10(exp 5) year time scales, cosmogenic radionuclide (CRN) concentrations have been gaining increasingly widespread usage (Brown et al., 1995; Riebe et al., 2004; Riebe et al., 2001; Vance et al., 2003). Most studies to date, however, have been conducted in slowly eroding ranges. In rapidly eroding mountains where landslides deliver most of the sediments to the rivers, we hypothesized that CRN concentrations could be highly perturbed by the stochastic processes of landsliding. Therefore, we undertook the development of a numerical model that simulated the effects of both landsliding and grain-by-grain attrition within fluvial catchments. This modeling effort has shown the effects of catchment size and erosion rate on CRN concentrations and allows a prediction of where to sample to obtain the optimal erosion rate estimates using CRN techniques. Finally, we developed computational techniques to operate on DEMs to extract useful information that would enable quantification of climate-erosion interactions. In particular, we worked on rapid techniques to define catchments of any given range of sizes, to extract channel gradients, to combine precipitation information to calculate discharge, and to utilize various stream-power models to determine the erosional energy within any given catchment within a transect. We briefly describe results from Wheeler Ridge, the Kyrgyz Range, the Nepal Himalaya, and our numerical modeling.

Burbank, Douglas W.↗
DOE OSTI2014

Analysis of the Space Propulsion System Problem Using RAVEN

This paper presents the solution of the space propulsion problem using a PRA code currently under development at Idaho National Laboratory (INL). RAVEN (Reactor Analysis and Virtual control ENviroment) is a multi-purpose Probabilistic Risk Assessment (PRA) software framework that allows dispatching different functionalities. It is designed to derive and actuate the control logic required to simulate the plant control system and operator actions (guided procedures) and to perform both Monte- Carlo sampling of random distributed events and Event Tree based analysis. In order to facilitate the input/output handling, a Graphical User Interface (GUI) and a post-processing data-mining module are available. RAVEN allows also to interface with several numerical codes such as RELAP5 and RELAP-7 and ad-hoc system simulators. For the space propulsion system problem, an ad-hoc simulator has been developed and written in python language and then interfaced to RAVEN. Such simulator fully models both deterministic (e.g., system dynamics and interactions between system components) and stochastic behaviors (i.e., failures of components/systems such as distribution lines and thrusters). Stochastic analysis is performed using random sampling based methodologies (i.e., Monte-Carlo). Such analysis is accomplished to determine both the reliability of the space propulsion system and to propagate the uncertainties associated to a specific set of parameters. As also indicated in the scope of the benchmark problem, the results generated by the stochastic analysis are used to generate risk-informed insights such as conditions under witch different strategy can be followed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Comparison of Optimal Thermodynamic Models of the Tricarboxylic Acid Cycle from Heterotrophs, Cyanobacteria, and Green Sulfur Bacteria

We have applied a new stochastic simulation approach to predict the metabolite levels, energy flow, and material flux in the different oxidative TCA cycles found in E. coli and Synechococcus sp. PCC 7002, and in the reductive TCA cycle typical of chemolithoautotrophs and phototrophic green sulfur bacteria such as Chlorobaculum tepidum. The simulation approach is based on equations of state and employs an assumption similar to that used in transition state theory. The ability to evaluate the thermodynamics of metabolic pathways allows one to understand the relationship between coupling of energy and material gradients in the environment and the selforganization of stable biological systems, and it is shown that each cycle operates in the direction expected due to its environmental niche. The simulations predict changes in metabolite levels and flux in response to changes in cofactor concentrations that would be hard to predict without an elaborate model based on the law of mass action. In fact, we show that a thermodynamically unfavorable reaction can still have flux in the forward direction when it is part of a reaction network. The ability to predict metabolite levels, energy flow and material flux should be significant for understanding the dynamics of natural systems and for understanding principles for engineering organisms for production of specialty chemicals, such as biofuels.

Thomas, Dennis G.↗
DOE OSTI2017

Statistics of Reconnection-driven Turbulence

Magnetic reconnection is a process that changes magnetic field topology in highly conducting fluids. Within the standard Sweet–Parker model, this process would be too slow to explain observations (e.g., solar flares). In reality, the process must be ubiquitous as astrophysical fluids are magnetized and motions of fluid elements necessarily entail crossing of magnetic frozen-in field lines and magnetic reconnection. In the presence of turbulence, the reconnection is independent of microscopic plasma properties and may be much faster than previously thought, as proposed in Lazarian and Vishniac and tested in Kowal et al. However, the considered turbulence in the Lazarian–Vishniac model was imposed externally. In this work, we consider reconnection-driven magnetized turbulence in realistic three-dimensional geometry initiated by stochastic noise. We demonstrate through numerical simulations that the stochastic reconnection is able to self-generate turbulence through interactions between the reconnection outflows. We analyze the statistical properties of velocity fluctuations using power spectra and anisotropy scaling in the local reference frame, which demonstrates that the reconnection produces Kolmogorov-like turbulence, compatible with the Goldreich and Sridhar model. Anisotropy statistics are, however, strongly affected by the dynamics of flows generated by the reconnection process. Once the broad turbulent region is formed, the typical anisotropy scaling l{sub ∥}∝l{sub ⊥}{sup 2/3} is formed, especially for high resolution models, where the broader range of scales is available. The decay of reconnection outflows to turbulent-like fluctuations, characterized by different anisotropy scalings, strongly depends on the β plasma parameter. Moreover, the estimated reconnection rates are weakly dependent on the model resolution, suggesting that no external processes are required to make reconnection fast.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

A score-based diffusion model approach for adaptive learning of stochastic partial differential equation solutions

In this paper, we propose a novel framework for adaptively learning the time-evolving solutions of stochastic partial differential equations (SPDEs) using score-based diffusion models within a recursive Bayesian inference setting. SPDEs play a central role in modeling complex physical systems under uncertainty, but their numerical solutions often suffer from model errors and reduced accuracy due to incomplete physical knowledge and environmental variability. To address these challenges, we encode the governing physics into the score function of a diffusion model using simulation data and incorporate observational information via a likelihood-based correction in a reverse-time stochastic differential equation. This enables adaptive learning through iterative refinement of the solution as new data becomes available. To improve computational efficiency in high-dimensional settings, we introduce the ensemble score filter, a training-free approximation of the score function designed for real-time inference. Numerical experiments on benchmark SPDEs demonstrate the accuracy and robustness of the proposed method under sparse and noisy observations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods

Uncertainty quantification (UQ) plays a major role in verification and validation for computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive experiments. Within the structure-property linkage, crystal plasticity finite element method (CPFEM) models have been widely used since they are one of a few ICME toolboxes that allows numerical predictions, providing the bridge from microstructure to materials properties and performances. Several constitutive models have been proposed in the last few decades to capture the mechanics and plasticity behavior of materials. While some UQ studies have been performed, the robustness and uncertainty of these constitutive models have not been rigorously established. In this work, we apply a stochastic collocation (SC) method, which is mathematically rigorous and has been widely used in the field of UQ, to quantify the uncertainty of three most commonly used constitutive models in CPFEM, namely phenomenological models (with and without twinning), and dislocation-density-based constitutive models, for three different types of crystal structures, namely face-centered cubic (fcc) copper (Cu), body-centered cubic (bcc) tungsten (W), and hexagonal close packing (hcp) magnesium (Mg). Our numerical results not only quantify the uncertainty of these constitutive models in stress-strain curve, but also analyze the global sensitivity of the underlying constitutive parameters with respect to the initial yield behavior, which may be helpful for robust constitutive model calibration works in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Spiking computation and stochastic amplification in a neuron-like semiconductor microstructure

We have demonstrated the proof of principle of a semiconductor neuron, which has dendrites, axon, and a soma and computes information encoded in electrical pulses in the same way as biological neurons. Electrical impulses applied to dendrites diffuse along microwires to the soma. The soma is the active part of the neuron, which regenerates input pulses above a voltage threshold and transmits them into the axon. Our concept of neuron is a major step forward because its spatial structure controls the timing of pulses, which arrive at the soma. Dendrites and axon act as transmission delay lines, which modify the information, coded in the timing of pulses. We have finally shown that noise enhances the detection sensitivity of the neuron by helping the transmission of weak periodic signals. A maximum enhancement of signal transmission was observed at an optimum noise level known as stochastic resonance. The experimental results are in excellent agreement with simulations of the FitzHugh-Nagumo model. Our neuron is therefore extremely well suited to providing feedback on the various mathematical approximations of neurons and building functional networks.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Joint Modeling of Quasar Variability and Accretion Disk Reprocessing Using Latent Stochastic Differential Equations

Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing, trained with supervised learning on simulated LSST-like 10 yr quasar light curves. We encode the light curves using a transformer encoder, and the driving variability is reconstructed using latent stochastic differential equations, a physically motivated generative deep learning method that can model continuous-time stochastic dynamics. By embedding the physical processes of the driving signal and reprocessing into our network, we achieve a model that is more robust and interpretable. We demonstrate that our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wave bands, even for out-of-distribution driving signals. Our approach provides a powerful framework that can be adapted to solve other inverse problems in multivariate time series.

Fagin, Joshua [City Univ. of New York (CUNY), NY (↗