Engineering topics
Lee, Daniel
Publications and source records attributed to Lee, Daniel.
Eigenvector Continuation with Subspace Learning
A common challenge faced in quantum physics is finding the extremal eigenvalues and eigenvectors of a Hamiltonian matrix in a vector space so large that linear algebra operations on general vectors are not possible. There are numerous efficient methods developed for this task, but they generally fail when some control parameter in the Hamiltonian matrix exceeds some threshold value. In this Letter we present a new technique called eigenvector continuation that can extend the reach of these methods. The key insight is that while an eigenvector resides in a linear space with enormous dimensions, the eigenvector trajectory generated by smooth changes of the Hamiltonian matrix is well approximated by a very low-dimensional manifold. We prove this statement using analytic function theory and propose an algorithm to solve for the extremal eigenvectors. We benchmark the method using several examples from quantum many-body theory.
Infrared Photometric Properties of 709 Candidate Stellar Bowshock Nebulae
Arcuate infrared nebulae are ubiquitous throughout the Galactic Plane and are candidates for partial shells, bubbles, or bowshocks produced by massive runaway stars. We tabulate infrared photometry for 709 such objects using images from the Spitzer Space Telescope, the Wide-field Infrared Explorer, and the Herschel Space Observatory (HSO). Of the 709 objects identified at 24 or 22 μm, 422 are detected at the HSO 70 μm bandpass. Of these, only 39 are detected at HSO 160 μm. The 70 μm peak surface brightnesses are 0.5–2.5 Jy arcmin{sup −2}. Color temperatures calculated from the 24 to 70 μm ratios range from 80 to 400 K. Color temperatures from 70 to 160 μm ratios are systematically lower, 40–200 K. Both of these temperature are, on average, 75% higher than the nominal temperatures derived by assuming that dust is in steady-state radiative equilibrium. This may be evidence of stellar wind bowshocks sweeping up and heating—possibly fragmenting but not destroying—interstellar dust. Infrared luminosity correlates with standoff distance, R {sub 0}, as predicted by published hydrodynamical models. Infrared spectral energy distributions are consistent with interstellar dust exposed to either single radiant energy density, U=10{sup 3}--10{sup 5} (in more than half of the objects) or a range of radiant energy densities U {sub min} = 25 to U {sub max} = 10{sup 3}–10{sup 5} times the mean interstellar value for the remainder. Hence, the central OB stars dominate the energetics, making these enticing laboratories for testing dust models in constrained radiation environments. The spectral energy densities are consistent with polycyclic aromatic hydrocarbon fractions q{sub PAH}≲1% in most objects.
Stan : A Probabilistic Programming Language
Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can also be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
SERVIR: From Space to Village. A Regional Monitoring and Visualization System For Environmental Management Using Satellite Applications For Sustainable Development
NASA is committed to providing technological support and expertise to regional and national organizations for earth science monitoring and analysis. This commitment is exemplified by NASA's long-term relationship with Central America. The focus of these efforts has primarily been to measure the impact of human development on the environment and to provide data for the management of human settlement and expansion in the region. Now, NASA is planning to extend and expand this capability to other regions of the world including Africa and the Caribbean. NASA began using satellite imagery over twenty-five years ago to locate important Maya archeological sites in Mesoamerica and to quantify the affect of deforestation on those sites. Continuing that mission, NASA has partnered with the U.S. Agency for International Development (USAID), the World Bank, the Water Center for the Humid Tropics of Latin America and the Caribbean (CATHALAC) and the Central American Commission for Environment and Development (CCAD) to develop SERVIR (Sistema Regional de Visualizacion y Monitoreo), for the Mesoamerican Biological Corridor. SERVIR has become one of the most important aspects of NASA's geospatial efforts in Central America by establishing a common access portal for information that affects the lives, livelihood and future of everyone in the region. SERVIR, most commonly referred to as a regional visualization and monitoring system, is a scientific and technological platform that integrates satellite and other geospatial data sets to generate tools for improved decision-making capabilities. It has a collection of data and models that are easily accessible to earth science managers, first responders, NGO's (Non-Government Organizations) and a host of others. SERVIR is currently used to monitor and forecast ecological changes as well as provide information for decision support during severe events such as forest fires, red tides,and tropical storms. Additionally, SERVIR addresses the nine societal benefit areas of the Global Earth Observation System (GEOSS): disasters, ecosystems, biodiversity, weather, water, climate, health, agriculture and energy.
Advanced Software Development Workstation Project
The Advanced Software Development Workstation Project, funded by Johnson Space Center, is investigating knowledge-based techniques for software reuse in NASA software development projects. Two prototypes have been demonstrated and a third is now in development. The approach is to build a foundation that provides passive reuse support, add a layer that uses domain-independent programming knowledge, add a layer that supports the acquisition of domain-specific programming knowledge to provide active support, and enhance maintainability and modifiability through an object-oriented approach. The development of new application software would use specification-by-reformulation, based on a cognitive theory of retrieval from very long-term memory in humans, and using an Ada code library and an object base. Current tasks include enhancements to the knowledge representation of Ada packages and abstract data types, extensions to support Ada package instantiation knowledge acquisition, integration with Ada compilers and relational databases, enhancements to the graphical user interface, and demonstration of the system with a NASA contractor-developed trajectory simulation package. Future work will focus on investigating issues involving scale-up and integration.