INTERSECT-SDK (Python)
Interconnected Science Ecosystem - Software Development Kit (INTERSECT-SDK)
Drane, Lance↗
Engineering topics
Archibald, Rick
Publications and source records attributed to Archibald, Rick.
Haar-Like Wavelets on Hierarchical Trees
Here, discrete wavelet methods, originally formulated in the setting of regularly sampled signals, can be adapted to data defined on a point cloud if some multiresolution structure is imposed on the cloud. A wide variety of hierarchical clustering algorithms can be used for this purpose, and the multiresolution structure obtained can be encoded by a hierarchical tree of subsets of the cloud. Prior work introduced the use of Haar-like bases defined with respect to such trees for approximation and learning tasks on unstructured data. This paper builds on that work in two directions. First, we present an algorithm for constructing Haar-like bases on general discrete hierarchical trees. Second, with an eye towards data compression, we present thresholding techniques for data defined on a point cloud with error controlled in the $L$ $\infty$ norm and in a Hölder-type norm. In a concluding trio of numerical examples, we apply our methods to compress a point cloud dataset, study the tightness of the $L$ $\infty$ error bound, and use thresholding to identify MNIST classifiers with good generalizability.
97 MATHEMATICS AND COMPUTING↗
Leveraging Single-Page Applications for Seamless Scientific Workflows: DevSecOps Considerations
Single-page applications (SPAs) have become indispensable in modern frontend development, with widespread adoption in scientific applications. The process of creating a single-page web application development environment which accurately reflects the production environment isn’t always straightforward. Most SPA build systems assume configuration at build time, while DevSecOps engineers prefer runtime configuration. This paper suggests a framework-agnostic approach to address issues that encompass both development and deployment, but are difficult to tackle without knowledge in both domains.
Drane, Lance↗
Enabling Interconnected Science Workflows through an Adapter Approach
The INTERSECT Software framework project aims to create an open federated library that connects, coordinates, and controls systems in the scientific domain. It features the Adapter, a flexible and extensible interface inspired by the Adapter design pattern in object-oriented programming. By utilizing Adapters, the INTERSECT SDK enables effective communication and coordination within a diverse ecosystem of systems. This adaptability facilitates the execution of complex operations within the framework, promoting collaboration and efficient workflow management in scientific research. Additionally, the generalizability of Adapters and their patterns enhances their utility in other scientific software projects and challenges.
McGaha, Jesse↗
Improving Predictions Under Uncertainty of Material Plasma Device Operations
Understanding the properties of materials when exposed to various plasma temperatures and fluxes is essential to the building and operating of fusion reactors. The Material Plasma Exposure eXperiment (MPEX) is an instrument currently being developed by the Department of Energy (DOE) for this purpose. MPEX is expected to come online in stages over the next five years. Proto-MPEX, the predecessor to MPEX, operated from 2014 to 2021, and was designed to understand the generation of plasma temperatures and fluxes at orders of magnitude below what will be obtained by MPEX. This work uses the recently developed stochastic neural network (SNN), a machine learning technique capable of operating under uncertainty to provide a surrogate model for the Proto-MPEX device. We demonstrate that SNN outperforms Bayesian neural network (BNN), a standard in the field of machine learning with uncertainty. The development of a robust surrogate of the Proto-MPEX will aid in the commissioning and operation of the MPEX device.
Archibald, Rick↗
AI-Constrained Bottom-Up Ecohydrology and Improved Prediction of Seasonal, Interannual, and Decadal Flood and Drought Risks
Focal Areas: (2) Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system composed of a hierarchy of models (3)Insight gleaned from complex data (both observed & simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI
54 ENVIRONMENTAL SCIENCES↗
An effective online data monitoring and saving strategy for large-scale climate simulations
Large-scale climate simulation models have been developed and widely used to generate historical data and study future climate scenarios. These simulation models often have to run for a couple of months to understand the changes in the global climate over the course of decades. This long-duration simulation process creates a huge amount of data with both high temporal and spatial resolution information; however, how to effectively monitor and record the climate changes based on these large-scale simulation results that are continuously produced in real time still remains to be resolved. Due to the slow process of writing data to disk, the current practice is to save a snapshot of the simulation results at a constant, slow rate although the data generation process runs at a very high speed. This study proposes an effective online data monitoring and saving strategy over the temporal and spatial domains with the consideration of practical storage and memory capacity constraints. Finally, our proposed method is able to intelligently select and record the most informative extreme values in the raw data generated from real-time simulations in the context of better monitoring climate changes.
54 ENVIRONMENTAL SCIENCES↗