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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Klippy

Tools and scripts created to ease the packaging of Klippy (A component of Klipper, a GPL3 licensed project). // Note: This project interacts with/is based on Klipper; Klipper contributors are listed here: https://github.com/Klipper3d/klipper/graphs/contributors. // Note: This is being submitted to DOECODE as per instructions received after providing notification of creation of IP outside of work as part of a site-specific IP assignment agreement. This work was not funded by, nor was any work performed under any DOE contract.

Haun, Aaron↗
DOE OSTI2023

pnnl/i2x

Interconnection Innovation e-Xchange (i2x) Test Systems allows for scripted customizations of social behavior, recovery operations, investment decisions, and metrics in a simplified Python module. Public test systems allow for replication of results by other stakeholders

McDermott, Tom↗
DOE OSTI2023

Productivity frameworks for HPC

Productivity Frameworks for HPC will include container recipes, build recipes, continuous integration scripts, and other software aimed at testing the portability of containerized HPC software across platforms and interconnects. In particular, it tests the utility of bind-mounting at the MPI layer (rather than the underlying fabric layer) to leverage a standardized protocol and avoid various technical debt and vendor lock-in. Since MPIs are often ABI-incompatible, trampolines such as the open-source Wi4MPI will be tested when such cases arise.

Hanford, Nathan↗
DOE OSTI2023

BCS (CSP Beam Characterization System) [SWR-23-13]

The Beam Characterization System (BCS) is an image-processing tool for estimating heliostat tracking errors. A large mirror, or heliostat, reflects the sun onto a distant target. A carefully calibrated camera then takes an image of this target to capture the shape of the reflected beam. The software kit hosts a series of scripts to identify key features and run analysis on the captured images.

Kesseli, Devon↗
DOE OSTI2023

edit-initramfs.sh

edit-initramfs.sh is a simple script to extract, edit, and rebuild an initramfs image. This makes it easy to quickly make edits to the contents of an initramfs.

Hutter, TonyJ↗
DOE OSTI2023

Code for the manuscript titled "Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces"

We would like to disclose two scripts, written in Jupyter notebook, in which we implement the "Blackout Diffusion Process" described in the manuscript "Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces" (LA-UR-23-20509), to be submitted to the International Conference of Machine Learning (ICML). The abstract of the manuscript is append below. == Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used generate samples from Gaussian noise. However, real world data often takes place in discrete-state spaces, which occur in many scientific applications. Here, we develop a theoretical formulation for arbitrary discrete-state Markov processes in the forward diffusion process. We relate the theory to the existing continuous-state Gaussian diffusion and identify the corresponding reverse-time stochastic process and score function in the continuous-time setting, and the reverse-time mapping in the discrete-time setting. As an example of this framework, we introduce "Blackout Diffusion", which learns to produce samples from an empty image instead of from noise. Numerical experiments on the CIFAR-10 dataset confirm the feasibility of generative diffusion modeling in a discrete space. Generalizing from specific (Gaussian) forward processes to a more general framework also sheds light on how to interpret generative diffusion models and their mathematical structure, which we comment on.

Lin, Yen Ting↗
DOE OSTI2022

RNA virus annotation pipeline (RVAP) v1.0

The code consists of two scripts that assign functional domains to RNA virus proteins. They achieve this by aggregating the outputs of multiple freely available tools and clustering the domains into high hierarchy clusters.

Camargo, Antonio↗
DOE OSTI2023

Bifacial_Radiance v0.4.2 [SWR-18-03]

The bifacial_radiance software contains a series of Python wrapper functions to make working with RADIANCE easier, particularly for the PV researcher interested in bifacial PV performance. For more information, check out our documentation, Tutorials in the form of Jupyter Notebooks, or refer to our Wiki and Issues page. Installation https://youtu.be/4A9GocfHKyM This video shows how to install the bifacial_radiance software and all associated software needed. More info on the Wiki. Instructions are also shown below. For detailed instructions of how to install bifacial_radiance, you can also refer to the installation guide. A GUI has been added in version 3.0. The GUI reads/writes all input parameters necessary to run a simulation, and runs the specified simulation by calling the correct functions with the specified parameters. So no need to use a journal or a script! But you still need to install following the procedure below.

Deline, Chris↗
DOE OSTI2023

LEAD - LEArning Damage

LEAD is a suite of scripts to enable rapid estimation of model parameters for damage models for ductile materials using machine learning. In its current version, the only supported damage model is TEPLA, though others are planned. The data we train on are comprised of free surface velocity data as well as porosity data. Some details about our method can be found in the preprint LA-UR-23-20468 (arxiv.org/abs/2301.07790)

Blaschke, Daniel↗
DOE OSTI2023

Iterative multi-task learning and inference from seismic images

Seismic interpretation aims to extract quantitative and interpretable attributes from a seismic image produced using some migration method to inform characteristics of a subsurface reservoir or target of interest. Current paradigms for computing seismic attributes mostly rely on single-task algorithms. We develop an iterative, multi-task machine learning method to learn and infer multiple attributes from a seismic image. This method is composed of two stages: a multi-task inference stage and a multi-modal, multi-task refinement stage. The basic mechanism of this method is that we train a multi-task inference neural network (NN) to estimate a set of attributes, including a relative geological time (RGT), a denoised higher-resolution (DHR) seismic image, and multiple fault attributes (including probability, dip, and strike), from a low-resolution, noisy seismic image; then we input the inferred attributes to a multi-task refinement NN to enhance the raw inference results iteratively. The two multi-task NNs are trained separately based on synthetic seismic images and associated attributes generated by a geological modeling algorithm. The software we intend to release is a PyTorch implementation of this multi-task learning method for both 2D and 3D cases along with scripts to run the training/validation. The algorithm and software can be a useful tool for automatic seismic interpretation.

Gao, Kai↗
DOE OSTI2023

ML for microbiomes

The software provides machine learning analysis and visualization to detect patterns in microbiome data, including topic modeling, probabilistic graphical modeling, conventional machine learning methods, and deep learning. The software is written in python and R, it uses some python and R libraries as well as big open-source libraries like sklearn, networkX, pytorch (python), pgmpy (python), and bnlearn (R). It also has a script to use for MALLET and DTM (open-source packages for topic modeling, written in Java).

Kim, Anastasiia↗
DOE OSTI2024

Vistransformers Explained

The Vistransformers Explained library is a collection of python notebooks that demonstrate the internal mechanics and uses of visual-transformer (ViT) machine learning models. The code implements, with mild modifications, ViT models that have been made publicly available through publication and GitHub code. The value added by this code is in-depth explanations of the mathematics behind the sub-modules of the ViT models, including original figures. Additionally, the library contains the code necessary to implement and train the ViT models. The library does not include example training data for the models; instead, it would rely on users generating their own datasets. The code is based on the PyTorch python library. It does not include any files other than python scripts, modules, or notebooks.

Callis, Skylar↗
DOE OSTI2024

MRE-Code-Hub/extract_loggernet

This script is used to read the infinitely growing data files created by a Campbell Data Logger to extract hourly data and write the content to hourly output files.

Sivaraman, Chitra↗
DOE OSTI2023

Ground Water Age Predictor

Machine Learning script to predict groundwater ages based on auxiliary features in a publicly available dataset, based on publicly available software libraries. Code applied to data from the Groundwater Ambient Monitoring and Assessment (GAMA) program in California, including well location and construction information, chemical constituents and isotopic tracers, and land use metrics.

Chakraborty, Indrasis↗
DOE OSTI2021

pnnl/solubility-prediction-paper

We are using existing deep learning modeling architectures and data representation techniques, but we are consolidating the methods into a single set of training and evaluation scripts. Additionally, we test and evaluate the performance of the models on the largest set of solubility measurements available to date.

Central, PNNL Developer↗
DOE OSTI2018

pnnl/ZebrafishBMD

ZebrafishBMD: A software tool to perform benchmark dose calculations using toxicity screening data of high-throughput zebrafish assays. High-content imaging of larval zebrafish behavior can be used as a screening approach to rapidly evaluate the relative potential for chemicals to cause toxicity. However, most statistical methods applied to these data transform movement values to incidence-based “hits” and calculate lowest effect levels (LELs), which loses individual fish resolution of behavior and defies hazard ranking due to reliance on applied dose levels. We developed a parallelizable workflow to calculate benchmark dose (BMD) values from dynamic, high-content zebrafish behavior data that scales for high throughput chemical screening. To capture the zebrafish movement response from light to dark stimulus, we summarized time-dependent data using both area under the curve and the immediate change at the transition point into two novel metrics that characterized abnormal behavior as a function of chemical concentration. The BMD workflow was applied to calculate BMD10 values of 1,060 ToxCast chemicals for 24 zebrafish endpoints, including behavior, mortality and morphology. ZebrafishBMD implements the BMD workflow and provides Matlab scripts to read the data (stored in a specific structure) from a .mat file, to calculate the behavioral endpoint values from movement versus time data, to construct concentrationresponse curves, to perform curve-fitting, and to calculate BMD10 values.

Thomas, Dennis↗