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NASA NTRS2022

ExoVista: A Suite of Planetary System Models for Exoplanet Studies

Studies of future space- and ground-based exoplanet surveys often rely on models of planetary systems to simulate instrument response, estimate scientific yields, perform trade analyses, and study efficient observation strategies. Until now, no planetary system models contained all of the basic physics necessary to enable study with all of the major exoplanet detection methods. Here we introduce a suite of such models generated by a new tool, exoVista. The exoVista tool quickly generates thousands of models of quasi-self-consistent planetary systems around known nearby stars at scattered light wavelengths and efficiently records the position, velocity, spectrum, and physical parameters of all bodies as functions of time. The modeled planetary systems can be used to simulate surveys using the direct imaging, transit, astrometric, and radial velocity techniques, as well as the overlap of these different methods.

Christopher C Stark↗

NASA NTRSDate not supplied

Enabling Interoperability in Earth System Digital Twins (ESDT): Integrating Observations, Models, and AI for Actionable Insights Through NASA'S Intelligent Systems Technology Program

NASA’s Intelligent Systems Technology Program (IST) is driving a paradigm shift in Earth science through the development of Earth System Digital Twins (ESDT). These integrated information systems create a dynamic "digital replica" of the Earth by harmonizing continuous, multi-source observations with high-fidelity models and state-of-the-art artificial intelligence (AI) that enable “What now?”, “What next?”, and “What if?” scenario building. These scenarios are reflected in NASA IST’s series of ESDTs, from the Coastal Zone Digital Twin that integrates complex data on the current state of the Chesapeake Bay to the Terrestrial Environmental Rapid-Replication and Assimilation Hydrometeorological (TerraHydro) AI-based ESDT that forecasts water movement across Earth’s surface, to the Agriculture Land Information System (AgLIS) which can be used to assess optimal planting dates and crop yield estimates. By bridging the gap between vast data archives and actionable insights, these projects enable a system-of-systems approach to understanding complex, interacting Earth processes. This poster will highlight recent innovations and future directions from NASA’s ESDT initiatives: Continuous Data Assimilation & Multi-Source Fusion. A core requirement of the ESDT work is the transition from static models to dynamic "living" replicas. This involves creating frameworks for the continual assimilation of near-real-time data from uncoordinated, heterogeneous sources, including satellite observations and airborne assets, and ground-based Internet of Things (IoT) sensors. These systems link design, operational status, and environmental data, ensuring the digital twin accurately reflects the current state of the physical Earth system. High-Fidelity Hybrid Modeling & Computational Acceleration to enable interactive "what-if" explorations, programs are moving beyond traditional, slow physical solvers by developing fast surrogate machine learning models and Deep Generative Models (DGMs). These hybrid approaches use neural networks to emulate complex physics, such as cloud feedback or ocean dynamics, at a fraction of the original computing cost, often leveraging advanced hardware like Graphics Processing Units (GPUs) to achieve the necessary scale. Federated Ecosystems & Interoperable Frameworks rather than building isolated tools, NASA IST is moving toward federated ESDTs and reusable analytic collaborative frameworks. This theme focuses on interoperability standards and common ontologies that allow specialized digital twins to interact and share data. This system-of-systems architecture supports multi-discipline investigations, such as analyzing how upstream watershed changes impact downstream urban flooding or how wildfire emissions affect regional air quality. By leveraging these advancements, ESDTs empower researchers and decision-makers to conduct real-time analysis and run complex hypothetical scenarios, ultimately improving our understanding of Earth’s evolving systems and informing critical real-world applications.

Earth System↗

DOE OSTI2018

Temporal Graph Generation Based on a Distribution of Temporal Motifs

Generating a synthetic graph that is similar to a given real-world graph is a critical requirement for privacy preservation and benchmarking purposes. Various generative models attempt to generate static graphs similar to real-world graphs. However, generation of temporal graphs is still an open research area. We present a temporal-motif based approach to generate synthetic temporal graph datasets and results from three real-world use cases.

Purohit, Sumit↗

DOE OSTI2021

On the Stochastic Stability of Deep Markov Models

Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. DMMs using deep neural networks to parametrize the transition of Markov probability distributions have recently been shown to provide more expressiveness in modeling sequential data and dynamical system responses. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a rigorous analytical method to prove the necessary and sufficient conditions of DMM's stochastic stability. This task is achieved by spectral analysis of the efficiently computed Jacobians of probabilistic maps modeled by deep neural networks. We make theoretical connections between the eigenvalues of neural network's weights and the different activation function types used on the stability and overall dynamic behavior of DMMs with Gaussian distributions. We empirically substantiate our theoretical results on stochastic stability and eigenvalue spectra via several numerical experiments. Formal stability guarantees of DMMs can substantially improve their robustness and trustworthiness, necessary for reliable use in safety-critical real-world applications.

Drgona, Jan↗

DOE OSTI2021

On the Stochastic Stability of Deep Markov Models

Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a novel stability analysis method and provide sufficient conditions of DMM's stochastic stability. The proposed stability analysis is based on the contraction of probabilistic maps modeled by deep neural networks. We make connections between the spectral properties of neural network's weights and different types of used activation function on the stability and overall dynamic behavior of DMMs with Gaussian distributions. Based on the theory, we propose a few practical methods for designing constrained DMMs with guaranteed stability. We empirically substantiate our theoretical results via intuitive numerical experiments using the proposed stability constraints.

Drgona, Jan↗

DOE OSTI2026

A Benchmarking Framework for Evaluating Large Language Model Capabilities in Nuclear Reactor Safety Applications

Large language models (LLMs) are increasingly capable of answering technical questions, synthesizing domain knowledge, and supporting engineering workflows. For nuclear science and engineering, these capabilities require careful, domain-specific evaluation before they can be credibly incorporated into safety-related activities, regulatory review, or technical decision support. This paper presents preliminary results from benchmarking framework for evaluating LLM capabilities in nuclear contexts. The framework is organized into three evaluation categories: nuclear fundamentals, general dual-use knowledge, and plant specific knowledge. These categories are intended to distinguish general nuclear engineering competence from broader technical reasoning and more context-dependent nuclear knowledge. Initial evaluations focus on nuclear fundamentals using questions representative of the knowledge expected of a nuclear professional engineer. Results indicate that contemporary frontier models perform at a high level and substantially exceed the performance of older model generations, with some models approaching saturation of the current benchmark. These findings suggest both the rapid improvement of LLM capabilities in specialized technical domains and the need for more discriminating evaluation methods. The paper presents the benchmark structure, preliminary model-comparison results, and ongoing work. This work supports development of verifiable, responsible, and safety-conscious methods for assessing AI systems in nuclear engineering applications.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

NASA NTRS1976

Ocean foam generation and modeling

A laboratory investigation was conducted to determine the physical and microwave properties of ocean foam. Special foam generators were designed and fabricated, using porous glass sheets, known as glass frits, as the principal element. The glass frit was sealed into a water-tight vertical box, a few centimeters from the bottom. Compressed air, applied to the lower chamber, created ocean foam from sea water lying on the frit. Foam heights of 30 cm were readily achieved, with relatively low air pressures. Special photographic techniques and analytical procedures were employed to determine foam bubble size distributions. In addition, the percentage water content of ocean foam was determined with the aid of a particulate sampling procedure. A glass frit foam generator, with pore diameters in the range 70 - 100 micrometers, produced foam with bubble distributions very similar to those found on the surface of natural ocean foam patches.

Porter, R. A.↗

DOE OSTI2013

A Generic Biogeochemical Module for Earth System Models: Next Generation BioGeoChemical Module (NGBGC), Version 1.0

Physical and biogeochemical processes regulate soil carbon dynamics and CO2 flux to and from atmosphere, influencing global climate changes. Integration of these processes into earth system models (e.g., community land models (CLM)), however, currently faces three major challenges: 1) extensive efforts are required to modify modeling structures and to rewrite computer programs to incorporate new or updated processes as new knowledge is being generated, 2) computational cost is prohibitively expensive to simulate biogeochemical processes in land models due to large variations in the rates of biogeochemical processes, and 3) various mathematical representations of biogeochemical processes exist to incorporate different aspects of fundamental mechanisms, but systematic evaluation of the different mathematical representations is difficult, if not possible. To address these challenges, we propose a new computational framework to easily incorporate physical and biogeochemical processes into land models. The new framework consists of a new biogeochemical module with a generic algorithm and reaction database so that new and updated processes can be incorporated into land models without the need to manually set up the ordinary differential equations to be solved numerically. The reaction database consists of processes of nutrient flow through the terrestrial ecosystems in plants, litter and soil. This framework facilitates effective comparison studies of biogeochemical cycles in an ecosystem using different conceptual models under the same land modeling framework. The approach was first implemented in CLM and benchmarked against simulations from the original CLM-CN code. A case study was then provided to demonstrate the advantages of using the new approach to incorporate a phosphorus cycle into the CLM model. To our knowledge, the phosphorus-incorporated CLM is a new model that can be used to simulate phosphorus limitation on the productivity of terrestrial ecosystems.

Fang, Yilin↗

NASA NTRS1985

The albedo field and cloud radiative forcing produced by a general circulation model with internally generated cloud optics

A general circulation model (GCM) study is presented in which cloud radiative properties are computed from cloud liquid water content inferred from the GCM hydrological cycle. Model-generated and satellite albedos are in rough agreement. Analysis of the cloud radiative forcing indicates that cloud albedo effects overcome cloud infrared opacity effects in most regions. Both computed and observed albedo of clouds decrease from low to high altitudes. The model with variable cloud optics produces significantly different regional albedos from the same one with fixed cloud optics, especially over the tropics. The cloud droplet size distribution also has a significant impact on the model albedos. The temperature of the tropical upper troposphere is somewhat sensitive to the microphysical characteristics of the model cirrus clouds.

Charlock, T. P.↗

NASA NTRS1985

Currents to the ionosphere from thunderstorm generators - A model study

A computer model (Nisbet, 1983) was used to evaluate the role of thunderstorm clouds in generating the currents necessary for the maintenance of the ionospheric potential. The effects of variations in the breakdown electric field below the lower charge center, heights of the upper and lower charge centers, generator currents and geometry, as well as of the conductivity profile of the cloud, the surrounding air, and the ionosphere, on the currents to the ionosphere were examined. It was shown that the most important single variable controlling the ionospheric current/generator current ratio for both very active storms and for storms that do not produce lightning is the height of the lower active center. The ionospheric conductivity has a negligible effect on the total current to the ionosphere, although it is very important in controlling the electric field and current density waveforms above the cloud.

Nisbet, J. S.↗

DOE OSTI2019

A Bayesian Approach to Real-Time Dynamic Parameter Estimation Using Phasor Measurement Unit Measurement

Here, we develop a polynomial-chaos-expansion (PCE)-based approach for decentralized dynamic parameter estimation through Bayesian inference. Using this approach, the non-Gaussian distribution of the inverted parameters is obtained. More specifically, we first represent the decentralized generator model with the PCE-based surrogate. This surrogate allows us to efficiently evaluate the time-consuming dynamic solver at parameter values through Metropolis-Hastings (M-H)-based Markov chain Monte Carlo (MCMC). Then, we propose a two-stage hybrid Markov chain Monte Carlo (MCMC) to recover a posteriori distribution of the decentralized generator model parameters. In the first stage, we use the gradient-enhanced Langevin MCMC algorithm to characterize an intermediate posterior parameter distribution. This algorithm is computationally scalable to the high-dimensional parameter space. Based on the intermediate posterior distribution, during the second stage, we use the adaptive MCMC algorithm to fine-tune the strong correlations between the parameters. Finally, the fully recovered a posterior distribution is obtained in the end. The simulation results show that the proposed PCE-based hybrid MCMC algorithm can accurately and efficiently estimate the high-dimensional generator dynamic model parameters with full probabilistic distribution provided.

42 ENGINEERING↗

DOE OSTI2010

Sampling graphs with a prescribed joint degree distribution using Markov Chains.

One of the most influential results in network analysis is that many natural networks exhibit a power-law or log-normal degree distribution. This has inspired numerous generative models that match this property. However, more recent work has shown that while these generative models do have the right degree distribution, they are not good models for real life networks due to their differences on other important metrics like conductance. We believe this is, in part, because many of these real-world networks have very different joint degree distributions, i.e. the probability that a randomly selected edge will be between nodes of degree k and l. Assortativity is a sufficient statistic of the joint degree distribution, and it has been previously noted that social networks tend to be assortative, while biological and technological networks tend to be disassortative. We suggest that the joint degree distribution of graphs is an interesting avenue of study for further research into network structure. We provide a simple greedy algorithm for constructing simple graphs from a given joint degree distribution, and a Monte Carlo Markov Chain method for sampling them. We also show that the state space of simple graphs with a fixed degree distribution is connected via endpoint switches. We empirically evaluate the mixing time of this Markov Chain by using experiments based on the autocorrelation of each edge.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

Evaluation of Thermolytic Hydrogen Generation Rate Models at High-Temperature/High-Hydroxide Regimes

This report describes the results of testing performed to extend the applicable ranges of temperature and hydroxide concentration for use within the Glycolate and Global Total Organic Carbon (TOC) Hydrogen Generation Rate (HGR) expressions. Seven experimental conditions (six simulants of the 242-25H Evaporator system chosen as a D-optimal set of experiments and a single test conducted at an elevated boiling point of 170 °C) were investigated in the presence of sodium glycolate and Xiameter TM AFE-1010. Glycolate was employed to study the extension of the Glycolate Thermolytic HGR expression while Xiameter TM AFE-1010 was employed to study the extension of the Global TOC Thermolytic HGR expression. The following conclusions were derived from this testing: The Glycolate Thermolytic HGR expression may be confidently used to predict thermolytic HGRs from glycolate at temperatures as high as 170 °C and hydroxide concentrations as high as 23 M.; The hydroxide and temperature-dependence predicted by the Global TOC Thermolytic HGR expression has been confirmed at temperatures as high as 170 °C and hydroxide concentrations as high as 23 M, suggesting that the Global TOC Thermolytic HGR expression may be used at these ranges.; Methane was observed from tests with Xiameter TM AFE-1010 at production rates higher than those observed for hydrogen. These rates were observed at temperatures higher than 100 °C.; Preliminary models suggest that increasing hydroxide/temperature causes an increase in Methane Generation Rate (MGR) from Xiameter TM AFE-1010. The following recommendations are based on this testing: The existing equations for thermolytic HGR from glycolate and non-glycolate organics should be used at Concentration, Storage, and Transfer Facilities (CSTF) storage and evaporation conditions, including temperatures and hydroxide concentrations exhibited in the 242-25H Evaporator.; Further investigation should be made into the influence of methylsilanes on CSTF flammability. This investigation should include: determination of the types of methylsilanes historically added to the CSTF, determination of methane formation rates from each type of methylsilane, and determination of the extent of degradation of methylsilanes in CSTF waste.; Characterization techniques should be developed by Savannah River National Laboratory (SRNL) to assist in the speciation of methylsilane-containing waste in the CSTF.; Additional testing with radioactive waste should be performed to determine the MGRs possible in radioactive waste and better inform model predictions made from testing with simulants.

08 HYDROGEN↗

DOE OSTI2026

Active learning enables generation of molecules that advance the known Pareto front

Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up to 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model.

Chemistry↗

NASA NTRS2001

Stochastic Modeling and Generation of Partially Polarized or Partially Coherent Electromagnetic Waves

Many new Earth remote-sensing instruments are embracing both the advantages and added complexity that result from interferometric or fully polarimetric operation. To increase instrument understanding and functionality a model of the signals these instruments measure is presented. A stochastic model is used as it recognizes the non-deterministic nature of any real-world measurements while also providing a tractable mathematical framework. A stationary, Gaussian-distributed model structure is proposed. Temporal and spectral correlation measures provide a statistical description of the physical properties of coherence and polarization-state. From this relationship the model is mathematically defined. The model is shown to be unique for any set of physical parameters. A method of realizing the model (necessary for applications such as synthetic calibration-signal generation) is given and computer simulation results are presented. The signals are constructed using the output of a multi-input multi-output linear filter system, driven with white noise.

Davis, Brynmor↗

DOE OSTI2021

Comparative Assessment of Data-driven Process Models in Health Information Technology

Process mining for conformance analysis consists of comparing a reference process model against a data-driven process model generated via log files from information technology systems. However, in the absence of a complete reference process model, we found no suggested approaches in the literature to address the need for evaluating process conformance among different healthcare facilities to assess standardization of care. Our goal is to find similarities and dissimilarities in data-driven process models among US Veterans Health Administration (VHA) facilities that can be indicative of patient safety issues. Our hypothesis was that the analysis would not produce statistically significant differences in outcome. We present a unique implementation of conformance analysis in process mining that consists of combining process mining, process mapping and statistical metrics. We illustrate our approach by applying it to the analysis of two clinical radiology order process models generated from healthcare data provided by two similar facilities in the VHA. The comparative assessment showed that about 70% of the orders completed successfully and 30% were not completed due to policy and duplications. Our analysis found a good statistical correlation between both facilities, as the Spearman’s correlation coefficient between facilities for the frequency of cases per total hours was 0.87879, for the frequency of cases by state transition was 0.79702 and for the throughput time per state transition was 0.63582. Additional statistical analyses using the Mann-Whitney U test and the root mean square error both produced values that were not significant. The foregoing approach validated our hypothesis by demonstrating a good statistical correlation of data describing the flow of clinical radiology orders absent a credible reference model. Finding good agreement between both facilities was important in confirming that the clinical orders flow in a similar manner, suggesting standardization of care.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2021

Eastern Equatorial Pacific Warming Delayed By Aerosols and Thermostat Response to Co2 Increase

Understanding the tropical Pacific response to global warming remains challenging. Here, we use a range of CMIP6 greenhouse warming experiments to assess the recent and future evolution of the equatorial Pacific east-west temperature gradient and corresponding Walker circulation. In abrupt CO2-increase scenarios many models generate an initial strengthening of this gradient resembling an ocean thermostat (OT), followed by a small weakening; other models generate an immediate weakening that becomes progressively stronger establishing a pronounced eastern equatorial Pacific (EP) warming pattern. The initial response in these experiments is a strong predictor for the future EP pattern simulated in both abrupt and realistic warming scenarios, but not in historical simulations showing no multi-model trend. The likely explanation is that the recent CO2-driven changes in the tropical Pacific are masked by aerosol effects and a potential OT-related delay, while the EP warming pattern will emerge as greenhouse gases overcome aerosol forcing.

Climate and Earth system modelling↗

DOE OSTI2015

Comparison of Uncertainty of Two Precipitation Prediction Models at Los Alamos National Lab Technical Area 54

Meteorological inputs are an important part of subsurface flow and transport modeling. The choice of source for meteorological data used as inputs has significant impacts on the results of subsurface flow and transport studies. One method to obtain the meteorological data required for flow and transport studies is the use of weather generating models. This paper compares the difference in performance of two weather generating models at Technical Area 54 of Los Alamos National Lab. Technical Area 54 is contains several waste pits for low-level radioactive waste and is the site for subsurface flow and transport studies. This makes the comparison of the performance of the two weather generators at this site particularly valuable.

54 ENVIRONMENTAL SCIENCES↗