SEARCH · Engineering Papers
Results for “high dimensionality”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Comparing Emulation Methods for Computer Models with High Dimensional Output
This Master’s Capstone project will present a comparison of statistical models for computer simulation studies. The four models included in this comparison study were chosen for both their proven and diverse methodologies. We will present a case study on hurricane flood data in the Delaware bay which highlights the strengths and weaknesses of each model when applied to a very large spatial field. As computers have gotten faster, we have become interested in modeling increasingly large spatial fields in both size and resolution. Statistical algorithms that are able to efficiently handle these fields have never been more important. We therefore find this comparison to be extremely topical.
High-Dimensional Data-Driven Energy Optimization for MultiModal Transit Agencies
Transportation accounts for 28% of the total energy use in the United States and as such, it is responsible for immense environmental impact, including urban air pollution and greenhouse gas emissions, and may pose a severe threat to energy security. As we encourage mode shift from personal vehicles to public transit, it is important to consider that public transit systems still require substantial amounts of energy; for example, public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO 2 emission annually. As such it is absolutely crucial that we study the bottlenecks to energy efficiency in public transit and develop new algorithms that can help the public transit agencies, especially those that are still operating mixed fleets, which may consist of Electric vehicles (EVs), hybrids (HEVs), and internal combustion engine vehicles (ICEVs), optimize the operations by deciding which vehicles are assigned to serving which transit trips.
Scalable3-BO: Big Data meets HPC - A scalable parallel high-dimensional Bayesian optimization framework on supercomputers.
Abstract not provided.
Hyper-Differential Sensitivity Analysis: Managing High Dimensional Uncertainty in Large-Scale Optimization Problems.
Abstract not provided.
Exploring Bayesian Optimal Experimental Designs for High-dimensional Combustion Systems.
Abstract not provided.
Recognizing and Mitigating the Problems of High Dimensional Data.
Abstract not provided.
Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data.
Abstract not provided.
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data
Explore the source record for details and available documents.
Derivative-Based Global Sensitivity Analysis for Modelswith High-Dimensional Inputs and Functional Output
Explore the source record for details and available documents.
Entanglement Traffic Engineering in Quantum Optical Fiber Networks based on High-Dimensional Kerr Optical Frequency Combs
The purpose of the project was to explore the performance of entanglement protocols based on microresonator Kerr optical frequency combs. We have addressed these challenges by developing a rigorous frequency-bin approach that allowed us to determine an explicit steady-state density operator for quantum microcombs below threshold. Our novel approach has allowed us to derived an explicit formula for the density operator on the frequency-bin basis, and to propose a complete description of quantum Kerr combs regardless of the number of sidemodes or loss-induced coupling to the environment. This formalism also allows for the explicit determination of their fidelity, purity, and entropy. These results are expected to permit the exploitation of the full potential of entangled microcombs for quantum technology.
Low-Variance Hessian Estimation: Coordinating High-Dimensional Sparsification with Projective Integral Updates
We can improve network pruning by leveraging the loss-topography extraction techniques used by projective integral updates for variational inference. Low-variance Hessians facilitate more aggressive pruning by providing better loss approximations when a parameter is removed.
Final Technical Report: Probabilistic Learning, Dimension Reduction, and Spectral Representations for High-Dimensional Uncertainty Quantification
Final Technical Report
Advancing Climate Causal Discovery: Local Space-Time Stencil Learning for High-dimensional Data
Explore the source record for details and available documents.
Supervised Dimension Reduction (SuperDR) for Regression Models with High-Dimensional Response
Explore the source record for details and available documents.
Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes
Not provided.
High-Dimensional Compressive Scanning Transmission Electron Microscopy
Explore the source record for details and available documents.