SEARCH · Search NASA

Results for “probabilistic framework”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2025

HAMscope: a snapshot Hyperspectral Autofluorescence Miniscope for real-time molecular imaging

We introduce HAMscope, a compact, snapshot hyperspectral autofluorescence miniscope that enables real-time, label-free molecular imaging in a wide range of biological systems. By integrating a thin polymer diffuser into a widefield miniscope, HAMscope spectrally encodes each frame and employs a probabilistic deep learning framework to reconstruct 30-channel hyperspectral stacks (452-703 nm) or directly infer molecular composition maps from single images. A scalable multi-pass U-Net architecture with transformer-based attention and per pixel uncertainty estimation enables high spatio-spectral fidelity (mean absolute error ∼0.0048) at video rates. While initially demonstrated in plant systems, including lignin, chlorophyll, and suberin imaging in intact poplar and cork tissues, the platform is readily adaptable to other applications such as neural activity mapping, metabolic profiling, and histopathology. We show that the system generalizes to out-of-distribution tissue types and supports direct molecular mapping without the need for spectral unmixing. HAMscope establishes a general framework for compact, uncertainty-aware spectral imaging that combines minimal optics with advanced deep learning, offering broad utility for real-time biochemical imaging across neuroscience, environmental monitoring, and biomedicine.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Uncertainty quantification in MELCOR Safety analysis of ARIES reactor designs

MELCOR-TMAP is a combined thermal-hydraulics and tritium tracking code developed to simulate severe accident scenarios in fission and fusion power plants. Here, we demonstrate the results of MELCOR-TMAP analyses on historical ARIES program reference designs. By coupling MELCOR-TMAP with the open source RAVEN probabilistic risk analysis framework’s Bayesian UQ capabilities, we also demonstrate key uncertainties in material properties with the highest impact on tritium inventory and plant risk.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗

DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

GDSA framework, a computational framework for complex modeling problems in radioactive waste management

This paper details a computational framework to produce automated, graphical workflows, and how this framework can be deployed to support complex modeling problems like those in nuclear engineering. Key benefits of the framework include: automating previously manual workflows; intuitive construction and communication of workflows through a graphical interface; and automated file transfer and handling for workflows deployed across heterogeneous computing resources. This paper demonstrates the framework's application to probabilistic post-closure performance assessment of systems for deep geologic disposal of nuclear waste. However, the framework is a general capability that can help users running a variety of computational studies.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1995

Computer-Simulation Surrogates for Optimization: Application to Trapezoidal Ducts and Axisymmetric Bodies

Engineering design and optimization efforts using computational systems rapidly become resource intensive. The goal of the surrogate-based approach is to perform a complete optimization with limited resources. In this paper we present a Bayesian-validated approach that informs the designer as to how well the surrogate performs; in particular, our surrogate framework provides precise (albeit probabilistic) bounds on the errors incurred in the surrogate-for-simulation substitution. The theory and algorithms of our computer{simulation surrogate framework are first described. The utility of the framework is then demonstrated through two illustrative examples: maximization of the flowrate of fully developed ow in trapezoidal ducts; and design of an axisymmetric body that achieves a target Stokes drag.

Otto, John C.↗

NASA NTRS2016

Using Probabilistic Methods in Water Scarcity Assessments: A First Step Towards a Water Scarcity Risk Assessment Framework

Water scarcity -driven by climate change, climate variability, and socioeconomic developments- is recognized as one of the most important global risks, both in terms of likelihood and impact. Whilst a wide range of studies have assessed the role of long term climate change and socioeconomic trends on global water scarcity, the impact of variability is less well understood. Moreover, the interactions between different forcing mechanisms, and their combined effect on changes in water scarcity conditions, are often neglected. Therefore, we provide a first step towards a framework for global water scarcity risk assessments, applying probabilistic methods to estimate water scarcity risks for different return periods under current and future conditions while using multiple climate and socioeconomic scenarios.

water scarcity↗

NASA NTRS2020

Bayesian Framework For Bioburden Density Calculations To Perform Planetary Protection Probabilistic Risk Assessment

The planetary protection discipline aims to minimize the microbial contamination on spacecraft to prevent the inadvertent contamination of other planetary bodies, known as forward planetary protection (PP). Planetary protection probabilistic risk assessment (PRA) relies on two core methodologies-the contamination probability event tree analysis and statistical parameter estimation. Planetary protection engineers combine several techniques to estimate the bioburden present on spacecraft components. A direct assay to enumerate CFU (colony forming units) is the preferred methodology, but given a similar processing environment the bioburden present on certain components is inferred using: (1) a NASA defined bioburden estimate based upon the biological cleanliness of the manufacturing/assembly environment or (2) sampled data from a similar spacecraft component. The paper presents an empirical Bayesian framework to systematically treat bioburden estimation and its uncertainties on different levels starting with measurement procedures to combining different components to subsystems and whole spacecraft. It is shown that the Bayesian approach can effectively handle estimations and their uncertainties at different levels and produce a reliable estimate for bioburden to be used to evaluate the probability of contamination.

Seuylemezian, Arman↗

NASA NTRS2020

Bayesian Framework for Bioburden Density Calculations to Perform Planetary Protection Probabilistic Risk Assessment

UNKNOWN

Seuylemezian, Arman↗

DOE OSTI2025

Dynamic probabilistic risk assessment and game theory for cyber security risk analysis in nuclear power plants

Nuclear Power Plants and energy systems have become more prone to cyber-attacks with their digitalization and the increased use of smart equipment. Hence, it is important to quantify the risk associated with cyber-attacks in such systems. Dynamic Probabilistic Risk Assessment which involves studying the evolution of a system due to random events and operator and attacker actions during a cyber-attack by employing a physics-based model of the system is a suitable framework to quantify cybersecurity risk in nuclear power plants. In addition to the plant dynamics, it is also important to model the strategies of the attackers and plant operators for an effective cybersecurity risk assessment. Game theory provides a set of necessary tools to model such strategic interactions. In this research, a framework that integrates dynamic probabilistic risk assessment with game theory for cybersecurity risk analysis in nuclear power plants is presented. The mathematical formulation is derived based on the theory of continuous event trees. We propose a game theory based action model, that utilizes physics-based rewards to define the strategies of attackers and operators at every decision epoch. As a case study, the risk associated with cyber-attacks on the digital components in the secondary side of a pressurized water reactor is studied using a reduced order model. A set of attacker actions and a set of operator actions are defined for the system. The operator and attacker interactions were modelled using simultaneous game, their action policies were computed using the concept of mixed strategy Nash equilibrium and the evolution of the system was studied.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2005

A model to assess the Mars Telecommunications Network relay robustness

The relatively long mission durations and compatible radio protocols of current and projected Mars orbiters have enabled the gradual development of a heterogeneous constellation providing proximity communication services for surface assets. The current and forecasted capability of this evolving network has reached the point that designers of future surface missions consider complete dependence on it. Such designers, along with those architecting network requirements, have a need to understand the robustness of projected communication service. A model has been created to identify the robustness of the Mars Network as a function of surface location and time. Due to the decade-plus time horizon considered, the network will evolve, with emerging productive nodes and nodes that cease or fail to contribute. The model is a flexible framework to holistically process node information into measures of capability robustness that can be visualized for maximum understanding. Outputs from JPL's Telecom Orbit Analysis Simulation Tool (TOAST) provide global telecom performance parameters for current and projected orbiters. Probabilistic estimates of orbiter fuel life are derived from orbit keeping burn rates, forecasted maneuver tasking, and anomaly resolution budgets. Orbiter reliability is estimated probabilistically. A flexible scheduling framework accommodates the projected mission queue as well as potential alterations.

Mars Telecommunications Orbiter (MTO)↗

DOE OSTI2025

A Sequential Model Predictive and Deep Reinforcement Learning-Based Controller for Distribution System Outage Mitigation under Hurricane Events

This paper proposes a proactive outage mitigation framework for power distribution networks to withstand hurricane-induced disruptions. It leverages Model Predictive Control (MPC) to identify safe lines for proactive switching during hurricanes, minimizing the risk of cascading failures and voltage violations. The switching strategies optimized by MPC are sequentially integrated with a Deep Reinforcement Learning agent using the Advantage Actor-Critic algorithm, enabling dynamic line switching to maximize connected buses and minimize voltage violations in real time. Using a probabilistic hurricane model, the framework predicts line failures and adapts to varying conditions to enhance grid resilience. Simulations on the IEEE 123-bus system demonstrate its effectiveness in maintaining high connectivity and minimizing disruptions. Real-time testing with an RTDS confirms the practicality and reliability of the proposed approach.

Selim, Alaa [University of Connecticut]↗

DOE OSTI2025

Micromechanical Surrogate Machine Learning Model for Creep Deformation Modeling

Process variability during the manufacture of gas turbine engine hot section components can significantly affect the material’s resulting microstructure. In casting, for instance, geometric variation within a component (thin sections versus thick sections, radial location) influences cooling rates and the resulting grain size. The high temperature creep response is known to be sensitive to grain size owing to a diffusional creep mechanism which occurs more readily along grain boundaries. Microstructural variation correspondingly drives mechanical behavior which propagates into component scale performance uncertainty. These factors are essential when planning inspection, maintenance, and repair strategies within a reliability framework. These benefits provide opportunities to increase overall energy efficiency through refined margins. Critically, there is an opportunity to bolster existing data-driven reliability models using physics-driven process-structure-property relations. Here we present recent work establishing a framework for evaluating the probabilistic creep performance of high-temperature materials. A novel microstructure-sensitive crystal plasticity finite element model is established that captures both grain boundary and crystallographic deformation effects. The computationally expensive physics model is calibrated using a statistical approach and this high-fidelity model is subsequently used to train a computationally efficient machine learning surrogate model. The surrogate model is essential for sampling a large ensemble of simulated structure-property pair results. The ensemble data are then mined to extract salient trends to be incorporated into a microstructure-sensitive reliability model. The proposed approach represents a novel way to capture microstructure-sensitive trends from physics-based models within a modern reliability framework.

Fernandez-Zelaia, Patxi [ORNL]↗

NASA NTRSDate not supplied

Evaluating Faulty State Occurrence in Wildfire UAS Missions Using Markov Chains

As autonomous technology advances, unmanned aircraft systems are increasingly integrated into emergency response missions, such as wildfire response. These systems must be be safe with less risk than non-autonomous counter parts, yet quantifying the risk associated with present-day and future systems conventionally relies solely on expert opinion and little data. Instead, combining narrative mishap reports with probabilistic analysis can provide a method for evolutionary and timely risk analysis. In this paper, we present a framework for a data-driven probabilistic risk assessment style analysis, where hazard events and rates originate from documented UAS mishaps. The framework is applied to a UAS mapping mission in wildfire response, including a fault tree analysis, event tree analysis, and probabilistic analysis using Markov Chains. The analysis provides an enumeration of hazards in the system, hazard events that can lead to faults, the probability of a mission experiencing any fault, the probability of experiencing a specific fault, and the expected time spent until faulty states occur in present-day operations.

risk analysis↗

NASA NTRSDate not supplied

Evaluating Faulty State Occurrence in Wildfire UAS Missions Using Markov Chains

risk analysis↗

DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Probabilistic Deliverability Assessment of Distributed Energy Resources via Scenario-Based AC Optimal Power Flow

As electric grids decarbonize and distributed energy resources (DERs) become increasingly prevalent, interconnection assessments must evolve to reflect operational variability and control flexibility. This paper highlights key modeling limitations observed in practice and reviews approaches for modeling uncertainty. It then introduces a Probabilistic Deliverability Assessment (PDA) framework designed to complement and extend existing procedures. The framework integrates scenario-based AC optimal power flow (AC OPF), corrective dispatch, and optional multi-temporal constraints. Together, these form a structured methodology for quantifying DER utilization, deliverability, and reliability under uncertainty in load, generation, and topology. Outputs include interpretable metrics with confidence intervals that inform siting decisions and evaluate compliance with reliability thresholds across sampled operating conditions. A case study on Puerto Rico’s publicly available bulk power system model demonstrates the framework’s application using minimal input data, consistent with current interconnection practice. Across staged fossil generation retirements, the PDA identifies high-value DER sites and regions requiring additional reactive power support. Results are presented through mean dispatch signals, reliability metrics, and geospatial visualizations, demonstrating how the framework provides transparent, data-driven siting recommendations. The framework’s modular design supports incremental adoption within existing workflows, encouraging broader use of AC OPF in interconnection and planning contexts.

14 SOLAR ENERGY↗

NASA NTRS2008

A Verification-Driven Approach to Control Analysis and Tuning

This paper proposes a methodology for the analysis and tuning of controllers using control verification metrics. These metrics, which are introduced in a companion paper, measure the size of the largest uncertainty set of a given class for which the closed-loop specifications are satisfied. This framework integrates deterministic and probabilistic uncertainty models into a setting that enables the deformation of sets in the parameter space, the control design space, and in the union of these two spaces. In regard to control analysis, we propose strategies that enable bounding regions of the design space where the specifications are satisfied by all the closed-loop systems associated with a prescribed uncertainty set. When this is unfeasible, we bound regions where the probability of satisfying the requirements exceeds a prescribed value. In regard to control tuning, we propose strategies for the improvement of the robust characteristics of a baseline controller. Some of these strategies use multi-point approximations to the control verification metrics in order to alleviate the numerical burden of solving a min-max problem. Since this methodology targets non-linear systems having an arbitrary, possibly implicit, functional dependency on the uncertain parameters and for which high-fidelity simulations are available, they are applicable to realistic engineering problems..

Crespo, Luis G.↗