Transfer learning of Gaussian processes to capture unmodeled physics: application to control of nonlinear dynamical systems.
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The Innovative Subsurface Learning and Hawaiian Exploration using Advanced Tomography (ISLAND HEAT) project aims to leverage and enhance existing Hawai'i geothermal play fairway analysis (PFA) results and validate a conceptual model-driven, optimized, least-cost exploration and geophysical suite at a proven geothermal field with known permeability, the Puna Geothermal Venture, located in the Lower East Rift Zone. The abundance of geophysical scrutinization before, during, and after the 2018 Kilauea eruption at and around the broader Puna system offers a rare opportunity to elucidate geophysical expressions within a dynamic magmatic rift setting. Development of the methodology continues with its application at a second prospective site identified by the PFA, Mauna Kea, which serves as confirmation of the integrated approach.
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The objective of this subcontract was to identify opportunities for the use of mixed precision within iterative solvers and to explore these opportunities both theoretically and experimentally. All quarterly milestones were achieved. Quarterly achievements are summarized in sections.
States are increasingly interested in gas distribution system planning and the role that demand-side resources can play in maintaining an affordable, reliable, equitable, and low-carbon energy system. In particular, public utility commissions and utilities are exploring the role of non-pipeline alternatives (NPA) – an investment or activity that defers, reduces, or avoids the need to construct or replace a pipeline – in gas distribution system planning. NPAs are an emerging cost and risk mitigation tool that can provide gas utilities with an opportunity to reduce emissions, gas system costs, and customer risk by avoiding unnecessary infrastructure spending. Rather than address system issues with more costly and long-lived traditional capital projects, utilities can leverage demand-side NPA resources such as energy efficiency and electrification as well as supply-side NPA resources to meet system needs. However, there is limited guidance on performing an NPA and even fewer lessons learned. To fill this gap, the U.S. Department of Energy funded two reports on NPAs. The first report is a literature review that focuses on four states' NPA policies. The second report offers a framework for evaluating NPAs that states can adapt to meet their policy needs.
Our overall objective is to improve and increase functional and structural predictions for a growing number of plant proteins of unknown structure and function (the Deep Green proteins), and to make our predictive data useful and accessible to the larger research community. The project is divided into five major objectives which include 1) Assembly and curation of Deep Green candidate protein sets; 2) in silico structural and functional predictions and network analyses; 3) assembly and validation of reverse genetic resources in Chlamydomonas reinhardtii (Chlamydomonas); 4) high throughput functional genomics characterization and prioritization in Chlamydomonas; and 5) structural validation of selected candidates and functional validation in two important reference plant species, Arabidopsis thaliana (Arabidopsis) and Setaria viridis (Setaria).
The objective of this subcontract was to identify opportunities for the use of mixed precision within iterative solvers and to explore these opportunities both theoretically and experimentally. All quarterly milestones were achieved. We summarize the achievements each quarter in sections below.