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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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Exploring the C P -violating Dashen phase in the Schwinger model with tensor networks
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Modeling the temporal network dynamics of neuronal cultures
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Optimization of Energy Flow through Synthetic Metabolic Modules and Regulatory Networks in a Model Photosynthetic Eukaryotic Microbe
Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methods, coupled with functional and synthetic genomics, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require a detailed systems biology understanding of photosynthetic processes and cellular metabolism. Genome-scale metabolic models (GEMs) are at the core of systems analysis of cellular processes and form a common organizational framework for analyses of data resulting from functional genomics experimental work and computational studies. Therefore, there is a clear demand for high quality photosynthetic model organisms and the appropriate computational tools that enable systems analysis of light-driven metabolism. Through research conducted we expanded the currently available repertoire of photosynthetic GEMs to include the commercially valuable model diatom Phaeoctylum tricornutum. Diatoms have a peculiar and distinct evolutionary footprint and represent a major eukaryotic lineage that is taxonomically and functionally distinct from green and red algae and vascular plants. Therefore, the true potential for light-driven metabolism aimed at biofuel production remains poorly understood at a systems level for a large subset of the global diversity of photosynthetic organisms. The metabolic capabilities of P. tricornutum were comparatively modeled with those from other photosynthetic groups in order to elucidate the occurrence of metabolic traits within and between phototrophs. Additionally, this research resulted in significant extension of the COnstraints Based Reconstruction and Analysis (COBRA) Toolbox to accommodate the crucial need for infrastructure required for ‘omics data integration and analysis in the context of genome-scale models. Therefore, the proposed research achieved two important goals. First, within the broad scope of photosynthetic organisms, we functionally compared and, as a result, identified cellular processes that require optimization in order to enable deployment as biofuel feedstock. Second, the proposed research resulted in development of key computational infrastructure, which can be further extended to other biological systems, that is currently lacking but necessary for multiple ‘omics data integration.
Machine learning of Physics-InformedGraph Neural Networks from TCAD models.
Abstract not provided.
Design, Synthesis, and Validation: Genome Scale Optimization of Energy Flux through Compartmentalized Metabolic Networks in a Model Photosynthetic Eukaryotic Microbe (Final Report)
Photosynthetic organisms have recently gained considerable attention for a role in development of renewable energy sources. Genome-enabled systems biology methodology and modeling, coupled with high throughput genome engineering strategies, present opportunities to develop sustainable and economical applications such as fuel production within the next 10 to 15 years. However, optimization of light-driven metabolism for biomass or biofuel production will require significant advances in methodological throughput as well as improvements in detailed systems biology understanding of photosynthetic processes and cellular metabolism. The ability of diatoms to thrive in upwelling-induced, periodically nutrient-rich conditions makes them the base for the world’s shortest and most energy-efficient food webs. Diatom photosynthesis is estimated to account for between 25% and 40% of the 45-50 billion tons of organic carbon fixed annually in the sea.
Transportability of a Convolutional Neural Network Seismic Denoising Model
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Graph Convolutional Neural Networks as Surrogate Models for Climate Simulation
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Uncertainty Quantification in Computational Science: from Physical Models to Neural Networks
Tutorial at the upcoming BIRS workshop https://www.birs.ca/events/2025/5-day-workshops/25w5381/schedule
Learning of Discrete Graphical Models with Neural Networks
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Designing and Modelling Analog Neural Network Training Accelerators.
Abstract not provided.
Empirical Insights for a Multi-Layered Network Complex Systems Model for Engineering Nuclear Security Systems.
Abstract not provided.
Empirical Insights for a Multi-Layered Network Complex Systems Model for Engineering Nuclear Security Systems-Slides.
Abstract not provided.
APPLICATION OF DEEP LEARNING NETWORKS TO SURROGATE MODELLING OF CRUD DEPOSITION
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Establishing Digital Twins for Non-Proliferation of Secure Facilities under Low Data Availability with Agent-Based Modeling, Deep Neural Network Surrogates, and Virtual Reality
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Hybrid-LLM-GNN: integrating large language models and graph neural networks for enhanced materials property prediction
This study combines Graph Neural Networks (GNNs) and Large Language Models (LLMs) to improve material property predictions. By leveraging both embeddings, this hybrid approach achieves up to a 25% improvement over GNN-only model in accuracy.