Neural Universal Differential Equation Hypernetwork Surrogates for Agent-Based Disease Models
Presentation for SIAM CSE 2025 on new surrogate modeling for epidemiological agent-based models
Robertson, Connor James [Sandia National Laborator↗
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Results for “surrogates”
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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DetSuM: Detector Surrogate Model for LArTPC
In large neutrino experiments such as the Deep Underground Neutrino Experiment (DUNE), estimating detector response uncertainties typically requires simulation samples that consume substantial computing resources and time. To mitigate this challenge, we present DetSuM, an uncertainty-aware surrogate model designed to capture the detector response variations with reduced computing load compared to full simulations. This poster describes the construction and evaluation of DetSuM using simulation and reconstruction datasets in a rare-event search at DUNE. We assess DetSuM's ability to predict key detector-response variations and their associated uncertainties, discuss current limitations, and outline improvements to extend its validity in systematics studies of DUNE physics.
Li, Aobo [UC, San Diego]↗
HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum System
Here, we introduce a hybrid computational framework that merges HPC-based numerical solvers with physics-informed ML surrogates for efficient modeling of magnon-photon interactions. By running short-duration, high-fidelity Maxwell-LLG simulations and feeding their results into an ML model, we substantially cut simulation time while achieving accurate predictions across larger spatiotemporal domains.
Accuracy↗
Dataset for SIAM MPI23 Project "Model inversion for complex physical systems using low-dimensional surrogates"
Training and testing data for the SIAM MPI23 workshop "Model inversion for complex physical systems using low-dimensional surrogates". The data consists of ensembles of input and output pairs corresponding to queries of a 2D saturated groundwater flow model. The inputs consist of random vectors which map to discretized model parameter fields via a Kosambi-Karhunen-Loève expansion. Output corresponds to discretized pressure fields.
basis adaptation, conditional Karhunen-Loève expan↗
Biphasic response of human iPSC-derived neural network activity following exposure to a sarin-surrogate nerve agent
Organophosphorus nerve agents (OPNA) are hazardous environmental exposures to the civilian population and have been historically weaponized as chemical warfare agents (CWA). OPNA exposure can lead to several neurological, sensory, and motor symptoms that can manifest into chronic neurological illnesses later in life. There is still a large need for technological advancement to better understand changes in brain function following OPNA exposure. The human-relevant in vitro multi-electrode array (MEA) system, which combines the MEA technology with human stem cell technology, has the potential to monitor the acute, sub-chronic, and chronic consequences of OPNA exposure on brain activity. However, the application of this system to assess OPNA hazards and risks to human brain function remains to be investigated. In a concentration-response study, we have employed a human-relevant MEA system to monitor and detect changes in the electrical activity of engineered neural networks to increasing concentrations of the sarin surrogate 4-nitrophenyl isopropyl methylphosphonate (NIMP). We report a biphasic response in the spiking (but not bursting) activity of neurons exposed to low (i.e., 0.4 and 4 μM) versus high concentrations (i.e., 40 and 100 μM) of NIMP, which was monitored during the exposure period and up to 6 days post-exposure. Regardless of the NIMP concentration, at a network level, communication or coordination of neuronal activity decreased as early as 60 min and persisted at 24 h of NIMP exposure. Once NIMP was removed, coordinated activity was no different than control (0 μM of NIMP). Interestingly, only in the high concentration of NIMP did coordination of activity at a network level begin to decrease again at 2 days post-exposure and persisted on day 6 post-exposure. Notably, cell viability was not affected during or after NIMP exposure. Also, while the catalytic activity of AChE decreased during NIMP exposure, its activity recovered once NIMP was removed. Gene expression analysis suggests that human iPSC-derived neurons and primary human astrocytes resulted in altered genes related to the cell’s interaction with the extracellular environment, its intracellular calcium signaling pathways, and inflammation, which could have contributed to how neurons communicated at a network level.
59 BASIC BIOLOGICAL SCIENCES↗
Quantitative Analysis of Fission-Product Surrogates in Molten Salt Chloride Aerosols
This work demonstrates laser-induced breakdown spectroscopy (LIBS) applied to a stream of aerosolized salt from molten eutectic LiCl-KCl. We demonstrate analytical capabilities to track fission-product surrogates of Cs, Sr, Pr, and Nd simultaneously, with application to monitor salts in pyroprocessing schemes and molten salt reactors. This work demonstrates limits of detection using LIBS on the order of 100 μg/g, which proves potentially applicable to monitoring fission-product concentrations in pyroprocessing applications. Additionally, this work explores fundamental aspects of plasma temperature and plasma electron density of the aerosolized species during LIBS with a specific focus on potential non-uniform plasma conditions in the aerosol.
74 - ATOMIC AND MOLECULAR PHYSICS↗
MMMnet: A Neural Network Surrogate for Real-Time Transport Prediction Based on the Updated Multi-Mode Model
The Multi-Mode Model (MMM) is a physics-based anomalous transport model integrated into TRANSP for predicting electron and ion thermal transport, electron and impurity particle transport, and toroidal and poloidal momentum transport. While MMM provides valuable predictive capabilities, its computational cost, although manageable for standard simulations, is too high for real-time control applications. MMMnet, a neural network-based surrogate model, is developed to address this challenge by significantly reducing computation time while maintaining high accuracy. Trained on TRANSP simulations of DIII-D discharges, MMMnet incorporates an updated version of MMM (9.0.10) with enhanced physics, including isotopic effects, plasma shaping via effective magnetic shear, unified correlation lengths for ion-scale modes, and a new physics-based model for the electromagnetic electron temperature gradient mode. A key advancement is MMMnet’s ability to predict all six transport coefficients, providing a comprehensive representation of plasma transport dynamics. MMMnet achieves a two-order-of-magnitude speed improvement while maintaining strong correlation with MMM diffusivities, making it well-suited for real-time tokamak control and scenario optimization.
DIII-D↗
Surrogate Modeling of Landau Damping with Deep Operator Networks
Kinetic simulations excel at capturing microscale plasma physics phenomena with high accuracy, but their computational demands make them impractical for modeling large-scale space and astrophysical systems. In this context, we build a surrogate model, using Deep Operator Networks (DeepONets), based upon the Vlasov–Poisson simulation data to model the dynamical evolution of plasmas, focusing on the Landau damping process—a fundamental kinetic phenomenon in space and astrophysical plasmas. The trained DeepONets are able to capture the evolution of electric field energy in both linear and nonlinear regimes under various conditions. Extensive validation highlights DeepONets’ robust performance in reproducing complex plasma behaviors with high accuracy, paving the way for large-scale modeling of space and astrophysical plasmas.
plasma astrophysics↗
A Study of a Surrogate Nuclear Thermal Propulsion Fuel Element
We report a surrogate fuel, Zirconium Carbide/Titanium Nitride has been prepared using a simple method to obtain a more uniform distribution of titanium nitride in the zirconium carbide matrix. Powders were sintered using Spark Plasma Sintering at 1750oC and 1850oC using a modified sintering profile. This resulted in a fairly uniform distribution of titanium nitride in the zirconium carbide matrix with a density of 95% of theoretical at 1850oC. Scanning electron microscopy, indentation hardness and x-ray diffraction results are given.
36 MATERIALS SCIENCE↗
Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science
This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.
Artificial Intelligence (cs.AI)↗
Technical note: Deep learning for creating surrogate models of precipitation in Earth system models
Abstract. We investigate techniques for using deep neural networks to produce surrogatemodels for short-term climate forecasts. A convolutional neural network istrained on 97 years of monthly precipitation output from the 1pctCO2 run (theCO 2 concentration increases by 1 % per year) simulated by the second-generation Canadian Earth System Model (CanESM2). The neural network clearly outperforms a persistence forecast anddoes not show substantially degraded performance even when the forecast lengthis extended to 120 months. The model is prone to underpredicting precipitationin areas characterized by intense precipitation events. Scheduled sampling(forcing the model to gradually use its own past predictions rather than groundtruth) is essential for avoiding amplification of early forecasting errors.However, the use of scheduled sampling also necessitates preforecasting(generating forecasts prior to the first forecast date) to obtain adequateperformance for the first few prediction time steps. We document the trainingprocedures and hyperparameter optimization process for researchers who wish toextend the use of neural networks in developing surrogate models.
54 ENVIRONMENTAL SCIENCES↗
Surrogate-equation technique for simulation of steady inviscid flow
A numerical procedure for the iterative solution of inviscid flow problems is described, and its utility for the calculation of steady subsonic and transonic flow fields is demonstrated. Application of the surrogate equation technique defined herein allows the formulation of stable, fully conservative, type dependent finite difference equations for use in obtaining numerical solutions to systems of first order partial differential equations, such as the steady state Euler equations. Steady, two dimensional solutions to the Euler equations for both subsonic, rotational flow and supersonic flow and to the small disturbance equations for transonic flow are presented.
Johnson, G. M.↗
Surrogate oracles, generalized dependency and simpler models
Software reliability models require the sequence of interfailure times from the debugging process as input. It was previously illustrated that using data from replicated debugging could greatly improve reliability predictions. However, inexpensive replication of the debugging process requires the existence of a cheap, fast error detector. Laboratory experiments can be designed around a gold version which is used as an oracle or around an n-version error detector. Unfortunately, software developers can not be expected to have an oracle or to bear the expense of n-versions. A generic technique is being investigated for approximating replicated data by using the partially debugged software as a difference detector. It is believed that the failure rate of each fault has significant dependence on the presence or absence of other faults. Thus, in order to discuss a failure rate for a known fault, the presence or absence of each of the other known faults needs to be specified. Also, in simpler models which use shorter input sequences without sacrificing accuracy are of interest. In fact, a possible gain in performance is conjectured. To investigate these propositions, NASA computers running LIC (RTI) versions are used to generate data. This data will be used to label the debugging graph associated with each version. These labeled graphs will be used to test the utility of a surrogate oracle, to analyze the dependent nature of fault failure rates and to explore the feasibility of reliability models which use the data of only the most recent failures.
Wilson, Larry↗
A Rigorous Framework for Optimization of Expensive Functions by Surrogates
The goal of the research reported here is to develop rigorous optimization algorithms to apply to some engineering design problems for which design application of traditional optimization approaches is not practical. This paper presents and analyzes a framework for generating a sequence of approximations to the objective function and managing the use of these approximations as surrogates for optimization. The result is to obtain convergence to a minimizer of an expensive objective function subject to simple constraints. The approach is widely applicable because it does not require, or even explicitly approximate, derivatives of the objective. Numerical results are presented for a 31-variable helicopter rotor blade design example and for a standard optimization test example.
Booker, Andrew J.↗
Software Surrogate
In 1994, Blackboard Technology received a NASA Phase I SBIR award entitled "A Blackboard-Based Framework for Mixed-Initiative, Crewed- Space-System Applications." This research continued in Phase II at JSC, where a generic architecture was developed in which a software surrogate serves as the operator's representative in the fast-paced realm of nearly autonomous, intelligent systems. This SBIR research effort addressed the need to support human-operator monitoring and intervention with intelligent systems such as those being developed for NASA's crewed space program.
Source record↗
Experimental Study of the Angle of Repose of Surrogate Martian Dust
Accumulation of wind-blown dust particles on solar cells and instruments will be a great challenge in the exploration of Mars, significantly reducing their lifetime, durability, and power output. For future Mars Lander missions it is crucial to gain information about the ideal angle at which solar panels can be positioned to minimize dust deposition and thus, maximize the power output and lifetime of the solar cells. The major determinant for the optimal panel angle is the angle of repose of the dust particles that is dependent on a variety of physical and chemical properties of the particles, the panel surface, and the environmental conditions on the Mars surface. To gain a basic understanding of the physical and chemical processes that govern dust deposition and to get feedback for the design of an experiment suitable for one of the future Mars Lander missions we simulate atmospheric conditions expected on the Mars surface in a controlled chamber, and observe the angle of repose of Mars dust surrogates. Dust deposition and angle of repose were observed on different sized spheres. To cover a range of potential materials we will use spheres made of 7075 aluminum (10 mm, and 15 mm), alumina oxide ceramic (10 mm), and Teflon(trademark) (10 mm) and wafers of gallium arsenide, silicon.
Moeller, L. E.↗
Myotonometry as a Surrogate Measure of Muscle Strength
Space flight-induced muscle atrophy/neuromuscular degradation and the consequent decrements in crew-member performance are of increasing concern as mission duration lengthens, and planetary exploration after extended space flight is planned. Pre- to post-flight strength measures have demonstrated that specific countermeasures, such as resistive exercise, are effective at countering microgravity-induced muscle atrophy and preventing decrements in muscle strength. However, in-flight assessment/monitoring of exercise countermeasure effectiveness will be essential during exploration class missions due to their duration. The ability to modify an exercise countermeasure prescription based on such real-time information will allow each individual crew member to perform the optimal amount and type of exercise countermeasure to maintain performance. In addition, such measures can be used to determine if a crew member is physically capable of performing a particular mission-related task during exploration class missions. The challenges faced in acquiring such data are those common to all space operations, namely the requirement for light-weight, low power, mechanically reliable technologies that make valid measurements in microgravity, in this case of muscle strength/neuromuscular function. Here we describe a simple, light-weight, low power, non-invasive device, known as the Myotonometer, that measures tissue stiffness as an indirect measure of muscle contractile state and muscle force production. Repeat myotonometer measurements made at the same location on the surface of the rectis femoris muscle (as determined using a 3D locator device, SEM plus or minus 0.34 mm) were shown to be reproducible over time at both maximal voluntary contraction (MVC) and at rest in a total of 17 sedentary subjects assessed three times over a period of seven days. In addition, graded voluntary isometric force production (i.e. 20%, 40%, 60%, 80% & 100% of MVC) during knee extension was shown to be significantly (p less than 0.01) correlated with contemporaneous myotonometer measurements made on the rectis femoris muscle in a total of 16 healthy subjects (8 males, 8 females). Further-more, this device has been operationally tested during parabolic flight demonstrating its suitability for use in a microgravity environment. Our data indicates that the Myotonometer is a viable surrogate measure of muscle contractile state/tone and of muscle strength/force production. Additional studies are required to assess the suitability of this technique for assessing these measures in de-conditioned subjects such as crew-members.
Ang, B. S.↗
Diagnosis of Magnetic Structures and Intermittency in Space Plasma Turbulence using the Method of Surrogate Data
Several observations in space plasmas have reported the presence of coherent structures at different plasma scales. Structure formation is believed to be a direct consequence of nonlinear interactions between the plasma modes, which depend strongly on phase synchronization of those modes. Despite this important role of the phases in turbulence, very limited work has been however devoted to study the phases as a potential tracers of nonlinearities in comparison with the wealth of literature on power spectra of turbulence where phases are totally missed. We present a method based on surrogate data to systematically detect coherent structures in turbulent signals. The new method has been applied successfully to magnetosheath turbulence (Sahraoui, Phys. Rev. E, 2008, in press), where the relationship between the identified phase coherence and intermittency (classically identified as non Gaussian tails of the PDFs) as well as the energy cascade has been studied. Here we review the main results obtained in that study and show further applications to small scale solar wind turbulence. Implications of the results on theoretical modelling of space turbulence (applicability of weak/wave turbulence, its validity limits and its connection to intermittency) will be discussed.
Sahraoui, Fouad↗