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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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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

PRISMA: PARALLEL REFINEMENT AND INTEGRATION SYSTEM FOR MULTI-AZIMUTHAL ANALYSIS

The Parallel Refinement and Integration System for Multi-azimuthal Analysis (PRISMA, version 1.1.0) is a Python application for processing X-ray diffraction (XRD) image data. PRISMA wraps GSAS-II to perform azimuthally-binned peak refinement, computes per-frame strain and d-spacing from those fits, and provides three PyQt5 graphical interfaces: (1) a Recipe Builder for selecting GSAS-II control (.imctrl) files, optional mask (.immask) files or threshold-ased masking, reference and experiment image sets, peaks, zimuthal range and bin size, and an optional ceria-based auto-calibration; (2) a Batch Processor that uses Dask on local workstations and pure MPI (mpi4py.futures.MPICommExecutor) on HPC to distribute GSAS-II refinement across cores or compute nodes and write results to a 4-dimensional (peaks x frames x azimuths x measurements) Zarr dataset; and (3) a Data Analyzer that renders heatmaps of fit parameters, strain, frame-to-frame deltas, and percent-change-vs-reference, and exports user-defined subsections to CSV or Excel. The peak-refinement algorithm is deterministic. Benchmark on ALCF Crux: a 20,000-image set, single-peak fit in frame mode with 44 azimuthal bins on 128 nodes x 128 workers, 48 seconds total wall time.

Lorenzo Martin, Maria De La Cinta [Argonne Nationa↗
DOE OSTI2026

Optimization Layers for Pyomo [SWR-25-132]

Optimization Layers for Pyomo solves an optimization problem using Pyomo and IPOPT during the forward pass. It computes the gradient of the optimal solution with respect to the parameters based on the KKT conditions in the backward pass. It is a Python library for constructing differentiable optimization layers in PyTorch from Pyomo optimization models.

Chen, Kejun [National Laboratory of the Rockies (N↗
DOE OSTI2026

ldrd_virus_work

This is a Python code base that takes openly-available genetic information on known viruses and performs supervised machine learning and feature importance analysis on the relationship of the viral genomes to the competence to infect humans or bind to a specific host cell receptor.

Reddy, Tyler [LANL]↗
DOE OSTI2026

swss-aurora

Python code for analyzing aurora data in the LANL Space Weather Summer School (O4985)

Holmes, Rebecca↗
DOE OSTI2026

GP Cosmology Surrogate v1.0

GP Cosmology Surrogate is a Python library for building and training a generalized multi-output Gaussian process (GP) framework of @takhtaganov2021cosmic. In this approach, the surrogate is constructed sequentially, guided by a Bayesian optimization acquisition function that targets reduction of emulation error in the regions most consistent with the observational data. This adaptive design concentrates computational resources where they have the greatest impact on inference accuracy. The library supports efficient training for separable GP kernels, which allows the use of Kronecker algebra to handle high-dimensional input spaces and large numbers of correlated outputs. This makes it well suited for applications such as modeling cosmological power spectra, large-scale physical simulations, and multi-output hyperparameter tuning. By combining scalable multi-output GP modeling with data-driven adaptive sampling, GPsurrogate enables parameter inference and optimization with substantially fewer simulations than conventional space-filling designs.

Lukic, Zarija [Lawrence Berkeley National Laborato↗
DOE OSTI2026

radkit base v1.6

The radkit (base) software suite (python) consists of three primary libraries: stark, trajan, and curie. The trajan library provides the tools to analyze and manipulate data from lidar and inertial measurement unit (IMU) devices, cameras, as well as trajectories from algorithms such as simultaneous localization and mapping (SLAM). These components allow reading and writing standard data formats, performing rigid affine transformations, discretizing three-dimensional space, and visualizing data products. The curie library comprises a standard set of object-oriented tools for radiation data and analysis in the following modules: (1) listmode and binmode data classes with methods for manipulation, plotting, slicing and file IO; (2) radiological/nuclear source detection/identification analysis results; (3) source encounters of correlated analyses and (4) energy-dependent angular detector response functions. The stark package provides low-level tools that are leveraged by both curie and trajan. The tools are flexible for offline analysis as well as performant for real-time integrations.

Salathe, Marco [Lawrence Berkeley National Laborat↗
DOE OSTI2026

ENPAX (ENergy Platform — Automated eXpenditures) [SWR-26-053]

ENPAX (ENergy Platform — Automated eXpenditures) is an open-source Python library that provides modular, bottom-up capital and operating expenditure (CAPEX/OPEX) models for energy technologies. Cost estimates are derived using system design characteristics, site specific details, power system considerations and infrastructure assumptions.

Mulas Hernando, Daniel [National Laboratory of the↗
DOE OSTI2026

TEC (ThermoElastoCreep) [SWR-25-167]

This repository provides a Python script to simulate the thermomechanical behavior of Concentrating Solar Power (CSP) receivers under periodic thermal loading. The simulation couples elastic and creep strains to predict stress relaxation, fatigue, and rupture life of the receiver structure. The work was done under the project Light Trapping Planar Cavity Receiver (LTPCR) funded by U.S. Department of Energy. All other relevant work and publications from LTPCR project can be found at https://github.com/NatLabRockies/LTPCR-Public

He, Xin [National Laboratory of the Rockies (NLR),↗
DOE OSTI2025

mphys-surrogate-model

This repository contains python scripts for building and studying reduced-order-modeling representations of droplet coalescence for eventual use in atmospheric models. The included data are generated from high-fidelity superdroplet methods and are utilized by machine learning pipelines to build data-driven models of droplet size distributions that evolve under coalescence. This repository further includes scripts to determine prediction (uncertainty) intervals on the data-driven model products based on conformal prediction.

Katona, JonasE [Lawrence Livermore National Labora↗
DOE OSTI2026

EFBMC

SAND2026-23986O EFBMC performs elastic Bayesian model calibration by applying Bayesian statistics and functional analysis. The software provides Python, R, and MATLAB scripts that enable users to calibrate models. 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.

Tucker, J. Derek [Sandia National Lab. (SNL-CA), L↗
DOE OSTI2026

SANDWake3D

SAND2026-23958O SANDWake3D models atmospheric surface layers and turbine wakes using a 3D parabolic RANS solver. This Python-based tool uses an efficient method to simulate wakes, capturing complex wake behaviors such as wind shear, veer, and atmospheric stratification, naturally. It has been calibrated and validated against higher-fidelity large eddy simulations (LES), providing reliable results for wind energy and atmospheric research applications. 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.

SciDAC↗
DOE OSTI2026

torch-einshard v1.0

torch-einshard is a Python library for describing local and distributed PyTorch tensor computations with compact, einsum-like notation. Its expressions name logical axes, specify how they are sharded across a PyTorch DeviceMesh, and represent partial reductions. The library automatically performs contractions, permutations, reshaping, splitting, gathering, reduction, reduce-scatter, and repartitioning while preserving autograd. Additional features include sharding-aware FFTs, tensor rolls, halo exchange, sliding windows, 1D–3D convolutions, uneven-shard handling, parameter initialization and gradient management, and cost-based execution planning. It is designed for scientific machine learning and large-model workloads, including tensor-, sequence-, and spatial-parallel MLPs, attention, convolutions, and spectral operations. Compared with manually combining torch.einsum and distributed collectives, torch-einshard expresses both the mathematical operation and data placement in one readable formula. This reduces boilerplate and synchronization errors, keeps forward and backward communication consistent, and allows the library to select optimized collective strategies without changing model code.

Morozov, Dmitriy [Lawrence Berkeley National Labor↗
DOE OSTI2026

ssys

`ssys` is a Python toolkit for exact algebraic transformation of ordinary differential equation (ODE) models into canonical S-system or Generalized Mass Action (GMA) form. Given a model in Antimony or SBML format, `ssys` produces a mathematically equivalent representation. The transformation introduces auxiliary variables as needed to decompose a broad class of nonlinearities into products of power-law terms. The recast is exact: the original and transformed systems have identical dynamics on the invariant constraint manifold defined by auxiliary variable definitions, given consistent initial conditions.

Hlavacek, William [Los Alamos National Laboratory]↗
DOE OSTI2026

PYDICE

A new Python program has been created that calculates similarity metrics (E, ck), available as a web server. Users can post one or more Sensitivity Data Files (SDFs) to either calculate similarity metrics (returned as a JSON response packet) or to be converted into a different SDF format (returned as a zip file).

Holcomb, Andrew [Oak Ridge National Laboratory (OR↗
DOE OSTI2026

GAT (Grid Analysis Toolkit) [SWR-25-41]

Grid Analysis Toolkit (GAT) is a unified Python API and plotting for power system PCM and CEM results (Sienna, PLEXOS, ReEDS™). It's a toolkit for wrangling data for Bulk Grid Dispatch and Transmission Analysis. GAT aims to provide simplified access to PCM and CEM results in a standard format while also allowing raw data access to underlying datasets specific to the model. This software can also be found on PyPI at For plotting, GAT defaults to standard National Lab of the Rockies (NLR) color schemes and standard styles while allowing customization.

Webb, Micah [National Laboratory of the Rockies (N↗
DOE OSTI2026

elm-diagnostics

elm-diagnostics is a Python package for computing diagnostic analyses and visualizations for the E3SM Land Model (ELM) component and is meant to support new feature development in ELM. The tool reads model history files and performs quantitative analyses including budget-closure checking, variable transformations, temporal aggregations, and statistical summaries to support model evaluation, validation, and scientific interpretation. The framework is designed for extensibility, with modular architecture enabling straightforward addition of new diagnostic methods, derived variables, analysis types, visualization approaches, and model-specific adaptations

Hoffman, Matt [Los Alamos National Laboratory]↗
DOE OSTI2026

HITMAN

HITMAN (Hermite Interpolation of Trajectories and Measurement Synthesis for Analysis of Navigators) interpolates—or estimates the unknown values between known values—flight trajectories and generates synthetic inertial measurement unit (IMU) data using Hermite splines. This Python library provides modeling and simulation capabilities to synthesize inertial measurements from discrete trajectory points, enabling researchers to create exemplar datasets for evaluating navigation algorithms in various applications, including consumer devices like smartphones and vehicles. 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.

Walker II, Michael [Sandia National Lab. (SNL-CA),↗