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<\/figure>Identifying parameters for building high-density, additively-manufactured parts. <\/strong> It is a challenge to determine process parameters to use in building high-density metal parts with laser powder-bed fusion, especially for materials that have never been additively manufactured before. We show how simple simulations and experiments, combined with data analysis, can help guide the selection of optimal parameters.<\/p>\n<\/div><\/div>\n\n\n\n Select publications (available from Google Scholar<\/a>):<\/p>\n\n\n\n Data mining techniques for use in additive manufacturing.<\/strong> Laser powder bed fusion is a rich source of problems that can be addressed using data analysis techniques. These include i) feature selection and parallel plots to identify important process parameters; ii) sampling and surrogates for exploring the input design space and for solving inverse problems; iii) self-organizing maps to understand the solution to the inverse problem; and iv) Gaussian process regression for prediction with associated uncertainties. By incorporating these techniques into an efficient iterative approach that starts with simple experiments and simulations to understand the design space, and moves to progressively more complex experiments and simulations as we get closer to a solution, we can reduce the time it takes to address problems of interest.<\/p>\n<\/div><\/div>\n\n\n\n Select publications (available from Google Scholar<\/a>):<\/p>\n\n\n\n WindSENSE: Integrating wind energy on the power grid<\/strong> is a challenge, especially with increasing installed wind capacity, the presence of ramp events where the energy increases or decreases by a large amount in a short time, and the need to keep the grid balanced. Taking a data-oriented approach to the problem, we focused on two topics: i) how do we best make use of existing data; and ii) what new data should we collect to improve short-term and extreme-event weather forecasts. This was joint work with AWS TruePower, in collaboration with Bonneville Power Administration (BPA), California Independent System Operator (CaISO), and Southern California Edison (SCE).<\/p>\n<\/div><\/div>\n\n\n\n Select publications (available from Google Scholar<\/a>):<\/p>\n\n\n\n Identifying human settlements in remotely-sensed data: <\/strong>The automated production of maps of human settlement from recent satellite images is essential to studies of urbanization and population movement. The spectral and spatial resolution of such imagery is high enough for accurate identification of human settlements, but we need to process vast amounts of data quickly. We proposed using a multi-level approach that started with simple, but fast, techniques at the lowest level, and progressively moved to more complex approaches as we reduced the size of the data that required processing. We demonstrated our approach using IKONOS 4-band and panchromatic images.<\/p>\n<\/div><\/div>\n\n\n\n Select publications (available from Google Scholar<\/a>):<\/p>\n\n\n\n SBOR – Similarity-based object retrieval: <\/strong>Computer simulations generate vast quantities of spatio-temporal output that can be challenging to explore using visualization alone. A display of an interesting region in one simulation may well prompt a scientist to ask if there were other similar regions in the same or in other simulations. We borrowed ideas from the field of content-based image retrieval (also called query by image content) to demonstrate that we could indeed address such questions, provided we carefully extracted the features representing the region of interest.<\/p>\n<\/div><\/div>\n\n\n\n Select publications (available from Google Scholar<\/a>):<\/p>\n\n\n\n Validation of computer simulations<\/strong>: Validation is the process of checking how close a computer simulation is to reality, for example by comparing the simulation with experiments. Since the simulation output could be a two-dimensional unstructured grid, while the experimental data may be in the form of images, a direct comparison is not an option. Using the Richtmeyer-Meshkov instability as an example, we showed how we could extract features from both the simulation grid data and the noisy experimental images to validate the simulation.<\/p>\n<\/div><\/div>\n\n\n\n Select publications (available from Google Scholar<\/a>):<\/p>\n\n\n\n Identification of bent-double galaxies:<\/strong> Our first data set was from the FIRST (Faint Images of the Radio Sky at Twenty Centimeters) astronomy survey, where we considered the task of identifying galaxies with a bent-double morphology. Working with the FIRST catalog, which had been created by fitting elliptic Gaussians to the brighter image ‘blobs’, we extracted representative features for each galaxy for use in machine learning algorithms. This data set also formed a key test bed for our research in classification algorithms.<\/p>\n<\/div><\/div>\n\n\n\n Select publications (available from Google Scholar<\/a>):<\/p>\n\n\n\n Analysis of coherent structures:<\/strong> Coherent structures are a collection of neighboring points (grid points in a simulation or pixels in an image) that behave as a coherent whole. Analysis of the behavior of these structures over time can shed light on the phenomenon being simulated or observed. Our work in this area has focused on the definition and evolution of these structures in both experimental data from the NSTX and simulation data of the Rayleigh-Taylor instability. The latter comprised two of the largest data sets we analyzed at 30TB and 80TB.<\/p>\n<\/div><\/div>\n\n\n\n Select publications (available from Google Scholar<\/a>):<\/p>\n\n\n\n Classification of orbits in a Poincare plot: <\/strong>One of the analysis tasks in magnetic fusion is the classification of orbits in a Poincare plot into one of four types – quasiperiodic, island chain, separatrix, and stochastic – based on the shape of the orbit. Each orbit is represented by the (x,y) coordinates of the points, with an orbit consisting of a few thousand points, making this the smallest data set we analyzed. It was also the most challenging, making us realize that while our eyes can easily discern a pattern created by a few points, automating the identification of this pattern in code is far from trivial.<\/p>\n<\/div><\/div>\n\n\n\n <\/p>\n\n\n\n Select publications (available from Google Scholar<\/a>): <\/p>\n\n\n\n My work in applications provides insight into scientific data and addresses specific questions posed by domain scientists. Many factors, occurring individually or in combination, make this endeavor challenging, including the size of the data set, its quality, its complexity, and how clearly the questions being addressed have been articulated by the domain scientists. Solving aContinue reading “Applications”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":192,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"http:\/\/ckamath.org\/index.php\/wp-json\/wp\/v2\/pages\/199"}],"collection":[{"href":"http:\/\/ckamath.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/ckamath.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/ckamath.org\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/ckamath.org\/index.php\/wp-json\/wp\/v2\/comments?post=199"}],"version-history":[{"count":60,"href":"http:\/\/ckamath.org\/index.php\/wp-json\/wp\/v2\/pages\/199\/revisions"}],"predecessor-version":[{"id":486,"href":"http:\/\/ckamath.org\/index.php\/wp-json\/wp\/v2\/pages\/199\/revisions\/486"}],"up":[{"embeddable":true,"href":"http:\/\/ckamath.org\/index.php\/wp-json\/wp\/v2\/pages\/192"}],"wp:attachment":[{"href":"http:\/\/ckamath.org\/index.php\/wp-json\/wp\/v2\/media?parent=199"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
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