IDC Re-Engineering Phase 2 Data Model.
This document contains the Data Model generated from the model contained in Rational Software Architect.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
SEARCH · Engineering Papers
Results for “Generative Model”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Portfolio-Scale Optimization of Customer Energy Efficiency Incentive and Marketing: Cooperative Research and Development Final Report, CRADA Number CRD-13-535
North East utility National Grid (NGrid) is developing a portfolio-scale application of OpenStudio designed to optimize incentive and marketing expenditures for their energy efficiency (EE) programs. NGrid wishes to leverage a combination of geographic information systems (GIS), public records, customer data, and content from the Building Component Library (BCL) to form a JavaScript Object Notation (JSON) input file that is consumed by an OpenStudio-based expert system for automated model generation. A baseline model for each customer building will be automatically tuned using electricity and gas consumption data, and a set of energy conservation measures (ECMs) associated with each NGrid incentive program will be applied to the model. The simulated energy performance and return on investment (ROI) will be compared with customer hurdle rates and available incentives to A) optimize the incentive required to overcome the customer hurdle rate and B) determine if marketing activity associated with the specific ECM is warranted for that particular customer. Repeated across their portfolio, this process will enable NGrid to substantially optimize their marketing and incentive expenditures, targeting those customers that will likely adopt and benefit from specific EE programs.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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↗
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↗
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↗
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↗
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↗
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↗
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↗
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↗
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↗
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↗
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↗
Ab initio structural modeling of and experimental validation for Chlamydia trachomatis protein CT296 reveal structural similarity to Fe(II) 2-oxoglutarate-dependent enzymes
Chlamydia trachomatis is a medically important pathogen that encodes a relatively high percentage of proteins with unknown function. The three-dimensional structure of a protein can be very informative regarding the protein's functional characteristics; however, determining protein structures experimentally can be very challenging. Computational methods that model protein structures with sufficient accuracy to facilitate functional studies have had notable successes. To evaluate the accuracy and potential impact of computational protein structure modeling of hypothetical proteins encoded by Chlamydia, a successful computational method termed I-TASSER was utilized to model the three-dimensional structure of a hypothetical protein encoded by open reading frame (ORF) CT296. CT296 has been reported to exhibit functional properties of a divalent cation transcription repressor (DcrA), with similarity to the Escherichia coli iron-responsive transcriptional repressor, Fur. Unexpectedly, the I-TASSER model of CT296 exhibited no structural similarity to any DNA-interacting proteins or motifs. To validate the I-TASSER-generated model, the structure of CT296 was solved experimentally using X-ray crystallography. Impressively, the ab initio I-TASSER-generated model closely matched (2.72-{angstrom} C{alpha} root mean square deviation [RMSD]) the high-resolution (1.8-{angstrom}) crystal structure of CT296. Modeled and experimentally determined structures of CT296 share structural characteristics of non-heme Fe(II) 2-oxoglutarate-dependent enzymes, although key enzymatic residues are not conserved, suggesting a unique biochemical process is likely associated with CT296 function. Additionally, functional analyses did not support prior reports that CT296 has properties shared with divalent cation repressors such as Fur.
59 BASIC BIOLOGICAL SCIENCES↗
Solver for hardware based computing
Full-AC load flow constitutes a core computation in power system analysis. A performance gain with a hardware implementation of a sparse-linear solver using a Field Programmable Gate Array (FPGA) is achieved by use of a DC network emulation of the power system bus. Analog Behavioral Models (ABMs) are used in an efficient strategy for designing analog emulation engines for large-scale power system computation. A generator model is also developed using analog circuits for load flow emulation for power system analysis to reduce computation time. The generator model includes reconfigurable parameters using operational transconductance amplifiers (OTAs). The circuit module is used with other reconfigurable circuits, i.e., transmission lines and loads.
97 MATHEMATICS AND COMPUTING↗
Stability of a tachyon braneworld
Within the braneworld paradigm the tachyonic scalar field has been used to generate models that attempt to solve some of the open problems that physics faces nowadays, both in cosmology and high energy physics as well. When these field configurations are produced by the interplay of higher dimensional warped gravity with some matter content, braneworld models must prove to be stable under the whole set of small fluctuations of the gravitational and matter fields background, among other consistency tests. Here we present a complete proof of the stability under scalar perturbations of tachyonic thick braneworlds with an embedded maximally symmetric 4D space-time, revealing its physical consistency. This family of models contains a recently reported tachyonic de Sitter thick braneworld which possesses a series of appealing properties. These features encompass complete regularity, asymptotic flatness (instead of being asymptotically dS or AdS) even when it contains a negative bulk cosmological constant, a relevant 3-brane with dS metric which naturally arises from the full set of field equations of the 5D background (it is not imposed), qualitatively describing the inflationary epochs of our Universe, and a graviton spectrum with a single zero mode bound state that accounts for the 4D graviton localised on the brane and is separated from the continuum of Kaluza-Klein massive graviton excitations by a mass gap. The presence of this mass gap in the graviton spectrum makes the extra-dimensional corrections to Newton’s law decay exponentially. Gauge vector fields with a single massless bound state in its mass spectrum are also localised on this braneworld model a fact that allows us to recover the Coulomb’s law of our 4D world. All these properties of the above referred tachyonic braneworld together with the positive stability analysis provided in this work, constitute a firm step towards the construction of realistic cosmological models within the braneworld paradigm.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
SU-F-18C-08: A Validation Study of a Commercially Available Software Package's Absorbed Dose Estimates in a Physical Phantom
Purpose: This study assesses the accuracy of the absorbed dose estimates from CT scans generated by Monte Carlo (MC) simulation using a commercially available radiation dose monitoring software program. Methods: Axial CT studies of an anthropomorphic abdomen phantom with dose bores at a central location and 4 peripheral locations were conducted using a fixed tube current at 120 kV. A 100 mm ion chamber and a 0.6 cc ion chamber calibrated at diagnostic energy levels were used to measure dose in the phantom at each of the 5 dose bore locations. Simulations using the software program's Monte Carlo engine were run using a mathematical model of the anthropomorphic phantom to determine conversion coefficients between the CTDIvol used for the study and the dose at the location of the dose bores. Simulations were conducted using both the software's generic CT beam model and a refined model generated using HVL and bow tie filter profile measurements made on the scanner used for the study. Results: Monte Carlo simulations completed using the generalized beam model differed from the measured conversion factors by an absolute value average of 13.0% and 13.8% for the 100 mm and 0.6 cc ion chamber studies, respectively. The MC simulations using the scanner specific beam model generated conversion coefficients that differed from the CTDIvol to measured dose conversion coefficients by an absolute value average of 7.3% and 7.8% for the 100 mm and 0.6 cc ion chamber cases, respectively. Conclusion: A scanner specific beam model used in MC simulations generates more accurate dose conversion coefficients in an anthropomorphic phantom than those generated with a generalized beam model. Agreement between measured conversion coefficients and simulated values were less than 20% for all positions using the universal beam model.
07 ISOTOPE AND RADIATION SOURCES↗
Hierarchical semi-Markov models with duration-aware dynamics for activity sequences
Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models for activities that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-min resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov router learns the patterns of “which activity comes next.” Second, a semi-Markov hazard component explicitly models activity durations, capturing “how long” activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.
24 POWER TRANSMISSION AND DISTRIBUTION↗