SEARCH · Engineering Papers
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.
AMSLib provides infrastructure to tightly couple multi-scale physics simulation code with ML surrogate model inference. It provides a wholistic runtime execution paradigm to supports uncertainty quantification, surrogate model inference, persistent data storing throughout the execution of a simulation.
Atomistic simulation with artificial intelligence (AI) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials with optimized/targeted properties. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside of their training domain and typically fail catastrophically in the prediction of rare events in molecular dynamics (MD) simulations. For effective AL loops to distinguish the informative data from enhanced sampled configurations, we developed a decision engine by configurational similarity and uncertainty quantification (UQ) with data augmentation.
UQ4ML is a code repository for a set of tools for the development of robust machine learning methods, uncertainty quantification and explainability of machine learning methods. The goal of these tools is to develop more robust and statistically rigorous machine learning methods for scientific applications. These tools are developed in Python, a high-level programming language that takes advantage of the Python ecosystem of high-quality open-source packages for machine learning.
This software includes the Jupyter notebooks, model pretraining and evaluation code for the EXPERT 2.0 Human-AI Reasoning Engine V0.1. It supports pre-training a Human-AI model for reasoning over multi-layer network representations. It includes prompt-based evaluation framework in a Jupyter notebook for AI reasoning and Jupyter widgets with AI-based techniques for evidence generation and uncertainty quantification to support human-AI reasoning.
SAND2024-01324O MrHyDE, which stands for Multi-resolution Hybridized Differential Equations, is a general-purpose C++ package for the solution of coupled multiphysics and multiscale systems on massively parallel computing systems. MrHyDE is designed to enable moving beyond forward simulation for multiscale applications which includes optimization, control, uncertainty quantification, and stochastic inversion. The framework provides interfaces to several packages within the Trilinos framework and leverages automatic differentiation to enable adjoint capabilities for large-scale, gradient-based optimization. MrHyDE provides automated multiscale capabilities through a subgrid model interface and multiscale Dirichlet-to-Neumann maps. For extreme-scale applications, MrHyDE provides in situ data-compression algorithms to reduce memory requirements while maintaining performance. MrHyDE is a general-purpose, computational framework for the solution of multiscale and multiphysics applications. It uses a combination of structure-preserving, physics-compatible discretizations, fully implicit methods, multi-resolution schemes, or fully explicit methods. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
SSAPy is a python package allowing for fast and precise orbital modeling. SSAPy is designed with speed and accuracy in mind and offers the following capabilities: - A variety of integrators, including Runge-Kutta, SciPy, SGP4, etc. - Customizable force propagation models, including a variety of Earth gravity models, lunar gravity, radiation pressure, etc. - Multiple-hypothesis tracking (MHT) UCT linker - Vectorized computations - Short arc probabilistic orbit determination - Conjunction probability estimation - Uncertainty quantification - Monte Carlo data fusion - Support for multiple coordinate frames (with coordinate frame conversions)
Supports characterization of explosive events by providing estimates of device parameters such as yield, height of burst/depth of burial, geolocation, and event time with uncertainty quantification.
SOURCES is a code for computing neutron source rates and spectra from spontaneous fission (including delayed neutrons) and (alpha,n) reactions in homogeneous materials and (alpha,n) reactions in single-interface and two-interface geometries. SOURCES is a Los Alamos National Laboratory (LANL) code that is written in FORTRAN and distributed through the Radiation Safety Information Computation Center (RSICC). LANL’s last release of SOURCES to RSICC was SOURCES4C in 2002. This disclosure covers the latest version of SOURCES, SOURCES4D. This version adds sensitivity capabilities for (alpha,n) sources in homogeneous materials. Specifically, SOURCES4D writes new output that can be used to calculate, in post-processing, first and second derivatives of the (alpha,n) source rate density and spectrum with respect to nuclide densities in a homogeneous material and first derivatives of the (alpha,n) source rate density and spectrum with respect to nuclide stopping powers and (alpha,n) cross sections (nuclear data). These derivatives are useful for uncertainty quantification, predictive modeling, and other applications in neutron transport problems.
As magnetic confinement fusion energy gains traction internationally to enable abundant energy production, designing components for fusion systems is a pressing challenge. During the planned lifetime of a fusion device, components evolve in extreme environments and must withstand large, repeated thermal loads and bombardment by 14 MeV neutrons, plasma ions, and neutral particles (deuterium, tritium, and helium), corrosive conditions, etc. All these physical processes take place simultaneously, interact in intricate ways, and impose important constraints that can affect performance. Experimental data is rare and costly to obtain, making design particularly challenging. Predictive computational frameworks must be an integral part of an accelerated and cost-effective design process by modeling fusion system performance in simulated environments. To better understand component degradation and operational impacts on their performance, the Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER) is designed as an open-source, fully integrated, multiphysics, multiscale, NQA-1 compliant framework facilitating 3D, high-fidelity fusion system modeling. To that end, SALAMANDER is a MOOSE-based framework, and therefore leverages MOOSE upstream libraries such as PETSc and libMesh to deliver sophisticated finite element, finite volume, and nonlinear solver technology for fusion energy simulations. SALAMANDER couples MOOSE physics module capabilities—such as thermal hydraulics, heat conduction, Navier-Stokes, and thermomechanics—with tritium transport via TMAP8, neutronics via Cardinal, and nascent particle-in-cell capabilities. Direct simulation Monte Carlo methods will be used to address neutral transport near the walls. By coupling all these physics in an integrated application, SALAMANDER will enable high-fidelity modeling of irradiation levels and plasma exposure conditions of plasma facing components and their impact on heat and tritium distributions, as well as the resulting mechanical constraints experienced by the plasma facing components and performance of blanket systems. Furthermore, SALAMANDER will be particularly suited for engineering studies thanks to the stochastic tool module readily available in MOOSE, allowing for extended uncertainty quantification and risk analysis studies. It is also able to use computer-aided design (CAD) meshes to model complex geometries, which is indispensable for fusion systems. SALAMANDER therefore supports design, safety, engineering, and research projects for magnetic confinement fusion systems
This code is used to train and test machine learning models and generate results and plots presented in 2501.08524 [astro-ph.IM]. The code is written in python. The goal of this work is to train ML models trained on simulated images of strong gravitational lenses. The trained model can then quickly infer properties of the lensed objects with uncertainty quantification.
SAND2026-16700O Pypolymix builds stochastic surrogate models using a lightweight Python library. Researchers working with large-scale scientific models can use it to enhance optimization and uncertainty quantification studies. Pypolymix enables the efficient construction of surrogate models and improves analysis and performance of complex simulations. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
This package implements Bayesian Neural Network (BNN) based surrogate models for multi-modal data. These models are fit using a custom Variational Bayes procedure leveraging conjugate posteriors in the last layer of the network, offering more accurate predictions with better-calibrated uncertainty quantification than mean-field Variational Bayes.
PSUADE (Problem Solving testbed for Uncertainty Analysis and Design Exploration) is a mathematical software useful for performing uncertainty quantification and sensitivity analysis.
Code to reproduce results in "Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks".
The Flow Battery Cost and Commercialization Tool (FlowBaCC) is a python-based framework used to project the costs and commercialization timelines for redox flow batteries. FlowBaCC combines learning curves for component costs and performance with adoption curves for market diffusion to project future system costs. The tool uses the bottom-up, spreadsheet-based Battery Performance and Cost Model for Flow Batteries (BatPaC-Flow) as the engine to translate learning curve inputs into full system costs. FlowBaCC provides capital costs and levelized costs of energy storage. The tool enables scenario analysis, sensitivity analysis, and uncertainty quantification (via grid and Monte Carlo methods).