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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

AI for Earthquake Physics

The core LANL program sponsored by Office of Science, Basic Energy Science, Chemical Sciences, Geosciences, and Biosciences (DOE-BES-CSGB) and led by PI Johnson aims to research earthquake faults to advance fault physics and earthquake hazards. All work completed is required to be made publicly available through publications and open-source codes supporting the published results. All routines are/will-be written in open source python and applied to publicly available data sets. These routines will format data from input into models, develop and test modeling frameworks for the problems addressed, and produce figures applicable to peer-reviewed manuscripts. All work is reviewed for Los Alamos Unlimited Release before submitting to a journal. This summary encompasses recently completed work and work to be complete for the duration of the program.

Johnson, Christopher↗
DOE OSTI2025

brag

Basic RAG-A python application for retrieval augmented generation (RAG).

Lui, Arthur↗
DOE OSTI2025

efield_from_tdJ

The efield_from_tdJ.py Python tool is designed to compute the electric field from a general time-dependent current density (J) input.

Blaine, William [LANL]↗
DOE OSTI2025

HPB_strengthmodel

Python-implementation of the Hunter-Preston strength model, but with generalized drag coefficient B. For details, see D. N. Blaschke, A. Hunter, and D. L. Preston, Int. J. Plast. 131 (2020) 102750. This code was used to generate most of the figures in this paper.

Blaschke, Daniel N.↗
DOE OSTI2025

exfor_client

A lightweight Python client and CLI for interacting with the [EXFOR Web API](https://nds.iaea.org/exfor/x4guide/API/). This tool enables searching, retrieving, and parsing experimental nuclear data — including uncertainties, covariance information, and metadata — while preserving provenance.

Grosskopf, Mike [Los Alamos National Laboratory]↗
DOE OSTI2024

Continuous thermostat setpoint monitoring and correction (Thermostat setpoint correction) v1.0

The Continuous Thermostat Setpoint Monitoring and Correction software is a set of fault detection and correction algorithms that can be implemented in thermostats with two-way OpenAPIs. It is written in the Python language. The algorithms aim to detect the most common and impactful efficiency problems associated with thermostat setpoints - overly aggressive heating or cooling setpoints, incorrect schedules/setbacks, and overly narrow deadbands. These algorithms can automatically detect faults, and implement associated corrective actions to bring the system back to a state of efficient operation. The algorithms can run remotely in the cloud, and directly implemented by connected thermostat manufacturers, or by third party service providers. The software enables a lightweight cost-effective energy management strategy for HVAC systems. The solution is specially viable for small and medium sized commercial buildings, where a full scale building automation system and fault detection and diagnostic tools are often unavailable.

Granderson, Jessica↗
DOE OSTI2025

mvBayesPy

SAND2025-11476O The mvBayesPy tool is a Python package that performs multivariate Bayesian analysis on generic data. It includes tools for regression modeling, diagnosis, basis decomposition, sensitivity analysis and visualization. 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, James [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2025

3_wise_bears

This is a small set of python scripts and HTML that uses OpenAI API to control large language models (LLMs) working agentically to solve a posed question / problem. There are 3 agents and they work to get it "just right" by taking on various "roles" of friendly and adversarial critics. It repeats a number of times specified by the user, and then writes a report.

DeBardeleben, Nathan Andrew [Los Alamos National L↗
DOE OSTI2025

pyAXEAP1-GUI package

SF-25-076 pyAXEAP1-GUI package is an X-ray emission spectroscopy (XES) data analysis package in the Python language. It can transfer the collected images from a 2D detector into spectra and display XES and RXES data as 3Dimages, 2D images and 1D profiles.

Berno, Alexander↗
DOE OSTI2025

Hydropower Market Game

SF-25-121 The Hydropower Market Game is an interactive educational software developed to teach fundamental concepts of hydropower generation, operations, and its role within electricity markets. Developed in Python and powered by Pygame, the game combines narrative-driven learning with progressive, hands-on levels where players explore topics such as the relationship between water flow and power generation, dam operations, pumped-storage systems, market interactions, and environmental constraints.Designed to support outreach and workforce development initiatives, the game provides an intuitive and engaging way to build energy literacy and raise awareness of hydropower’s contribution to the nation’s energy system.

Ploussard, Quentin [Argonne National Laboratory (A↗
DOE OSTI2025

Graph-based Reversible Evaluation and Tangents Library

GRETL is a C++ library for evaluation, re-evaluation and algorithmic differentiation of functional operations on an arbitrary computational graph with limited memory usage. Similar to popular machine learning frameworks in Python, like PyTorch and JAX, it tracks and stores both operations and output data as functions are evaluated. Once this composition of functions is built up, the entire chain of operations can be back propagated to compute sensitivities of the final result with respect to any number of inputs. In contrast to most machine learning applications, memory usage becomes the bottleneck for back propagation in many physics applications, especially for time-dependent PDEs. Dynamic check pointing becomes essential. An important distinguishing feature of GRETL is its ability to limit the maximum memory usage by automatically dynamic checkpointing the data output for each graph operation (see Wang, Moin, Iaccarino, 2009). During backpropagation, parts of the graph that are no longer in memory are automatically re-evaluated from upstream checkpointed states as needed for derivative sensitivity calculations (or more precisely, for vector-Jacobian products). GRETL is particularly beneficial for applications, such as coupled multi-physics, where deriving adjoint-based sensitivities and managing checkpoint memory across modules becomes onerous. Cases which can be readily handled by the GRETL library include: different time-integration algorithms per physics (e.g., coupled predictor-corrector algorithms, IMEX, etc.), sub-cycling, asynchronous integrators, state dependent timestep sizes, iterative solvers and coupling algorithms, controller algorithms, and more.

Tupek, MichaelR [Lawrence Livermore National Labor↗
DOE OSTI2025

BATMODS-lite [SWR-25-108]

Battery Analysis and Training Models for Optimization and Design Studies (BATMODS) is a Python package with an API for pre-built battery models. The original purpose of the package was to quickly generate synthetic data for machine learning models to train with. However, the models are generally useful for any battery simulations or analysis. BATMODS-lite includes the following: 1) A library and API for pre-built battery models 2) Kinetic/transport properties for common battery materials

Randall, Corey [National Laboratory of the Rockies↗
DOE OSTI2026

cwru-sdle/CEMENTO

CEMENTO is a component python package of the larger SDLE FAIR application suite of tools for creating scientific ontologies more efficiently. This package provides functional interfaces for converting draw.io diagrams of ontologies into RDF triple file formats and vice versa. This package is able to provide term matching between reference ontology files and terms used in draw.io diagrams allowing for faster ontology deployment while maintaining robust cross-references.

Ponon, GabrielObsequio [Case Western Reserve Univ.↗
DOE OSTI2025

HALLUFIELD: DETECTING LLM HALLUCINATIONS VIA FIELD-THEORETIC MODELING

A research-focused Python package that implements our hallucination-detection method for large language models(LLMs). The code computes stability signals from LLM predictions across various hyperparameters sweeps and combines free-energy/entropy–style metrics to flag likely hallucinations, with tunable thresholds for batch scoring. The repo includes evaluation scripts, config files, and example notebooks to reproduce benchmark results and ablations; it depends on standard open-source libraries (PyTorch, Hugging Face) and runs on CPU/GPU. The repository examples only use public models/datasets only.

Bhattarai, Manish [Los Alamos National Labs]↗
DOE OSTI2026

ShenCFD

ShenCFD is a fast pseudospectral solver for fluid dynamics written to be maximally Pythonic and maximally useful for machine-learning-based turbulence model discovery.

Saenz, Juan↗
DOE OSTI2025

STARLIGHT

A Python package consisting of standard and novel methods for calculating electronic correlation effects and electrical conductivity in lattice models subject to strong magnetic fields.

Staros, Daniel [Los Alamos National Laboratory]↗
DOE OSTI2026

polars-dovmed (dovmed) v0.1.0

polars-dovmed is a python package for text search and extraction from NCBI's PubMed Central Open Access subset. It is powered by the polars dataframe library and leverages modern file formats (parquet) to efficiently scan public literature.

Roux, Simon [Lawrence Berkeley National Laboratory↗