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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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Establishing a data-driven deformation model for Tin using symbolic regression and genetic programming .
Abstract not provided.
A data-driven peridynamic continuum model for upscaling molecular dynamics.
Abstract not provided.
Learning Why: Data-Driven Causal Evaluations of Climate Models.
Abstract not provided.
Data-Driven Compact Modeling of Bipolar Junction Transistors with Recurrent Neural Networks.
Abstract not provided.
Using Monitoring Data to Improve HPC Performance via Network-Data-Driven Allocation.
Abstract not provided.
Data Driven Controls at WRRFs – A Plan for Ammonia-Based Aeration Control (ABAC)
An ammonia-based aeration controller (ABAC) is being developed in the form of a model predictive controller (MPC) at the HRSD Nansemond Treatment Plant. The project integrates the following concepts: machine learning, digital twin, ABAC and MPC.
Fermilab’s data driven approach with Affiliations to drive Science and business processes [Slides]
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A data-driven roadmap towards Pareto optimal hydrogen storage alloys.
Abstract not provided.
Integrative data-driven approaches for characterization & prediction of aerosol-cloud processes.
Abstract not provided.
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties.
Abstract not provided.
A Hybrid Physics-Based, Data-Driven Approach to Model Damage Accumulation in Corrosion of Polymeric Adhesives
The degradation of polymeric adhesives in dissimilar joints is a menace to electric and structural components of the automotive industry. In polymeric adhesives, failure often occurs due to damage accumulated from discrete environmental sources—thermal degradation, oxidation, hydrolysis, radiation, and moisture—that are combined with progressive damage mechanisms, such as aging and fatigue. Because most damage mechanisms that lead to degradation act in parallel, the estimation of the lifetime of a polymeric material becomes a significant reliability issue, no study currently exists that can simultaneously consider even two of these phenomena. Most current efforts are directed toward a single-phenomenon, single-model approach in which a model is developed to cover one specific damage mechanism. While this approach is advantageous in exploring single mechanisms, it is strongly inconclusive for degradation that occurs by multiple damage mechanisms. Our team plans to take a fresh perspective by designing a hybrid constitutive/data-driven approach that allows models of different damage mechanisms to be integrated into one model and then used to explore the degradation-induced failure of polymeric adhesives. The theoretical-computational part of this work is divided into four research components as described in the Approach section below.
Methods to Discover New Photocathode Materials using Machine Learning and Data-Driven Screening
This poster poster is accepted to be presented at the upcoming International Particle Accelerator Conference (www.ipac23.org)
Data-Driven Geometric Efficiency Correction for DUNE-PRISM Analysis
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Dynamics of He Bubbles during Thermal Annealing: A Data-driven Approach.
Abstract not provided.
Data-Driven Detection of Phase Changes in Evolving Distribution Systems.
Abstract not provided.