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You searched for subject:( sloppy models). Showing records 1 – 2 of 2 total matches.

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Cornell University

1. Chachra, Ricky. Science In High Dimensions: Multiparameter Models And Big Data .

Degree: 2014, Cornell University

Complex multiparameter models such as in climate science, economics, systems biology, materials science, neural networks and machine learning have a large-dimensional space of undetermined parameters as well as a large-dimensional space of predicted data. These high-dimensional spaces of inputs and outputs pose many challenges. Recent work with a diversity of nonlinear predictive models, microscopic models in physics, and analysis of large datasets, has led to important insights. In particular, it was shown that nonlinear fits to data in a variety of multiparameter models largely rely on only a few stiff directions in parameter space. Chapter 2 explores a qualitative basis for this compression of parameter space using a model nonlinear system with two time scales. A systematic separation of scales is shown to correspond to an increasing insensitivity of parameter space directions that only affect the fast dynamics. Chapter 3 shows with the help of microscopic physics models that emergent theories in physics also rely on a sloppy compression of the parameter space where macroscopically relevant variables form the stiff directions. Lastly, in chapter 4, we will learn that the data space of historical daily stock returns of US public companies has an emergent simplex structure that makes it amenable to a low-dimensional representation. This leads to insights into the performance of various business sectors, the decomposition of firms into emergent sectors, and the evolution of firm characteristics in time. Advisors/Committee Members: Strogatz, Steven H (committeeMember), Guckenheimer, John Mark (committeeMember).

Subjects/Keywords: stock markets; sloppy models; van der Pol

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Chachra, R. (2014). Science In High Dimensions: Multiparameter Models And Big Data . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/36155

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chachra, Ricky. “Science In High Dimensions: Multiparameter Models And Big Data .” 2014. Thesis, Cornell University. Accessed May 20, 2019. http://hdl.handle.net/1813/36155.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chachra, Ricky. “Science In High Dimensions: Multiparameter Models And Big Data .” 2014. Web. 20 May 2019.

Vancouver:

Chachra R. Science In High Dimensions: Multiparameter Models And Big Data . [Internet] [Thesis]. Cornell University; 2014. [cited 2019 May 20]. Available from: http://hdl.handle.net/1813/36155.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chachra R. Science In High Dimensions: Multiparameter Models And Big Data . [Thesis]. Cornell University; 2014. Available from: http://hdl.handle.net/1813/36155

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Cornell University

2. Kachuck, Samuel Benjamin. Time-domain glacial isostatic adjustment: theory, computation, and statistical applications .

Degree: 2018, Cornell University

The rocky interior of the Earth flows viscoelastically over timescales on the order of 1000 years in response to sustained stresses. Such flow is still occurring today as a result of the growth and collapse over the last ice age of massive ice sheets and is evident in changes of the Earth's surface and gravity, a process called glacial isostatic adjustment (GIA). This thesis presents a new technique for computing this viscoelastic deformation and statistical methods for more efficiently inferring properties of the Earth's mantle and the deglaciation from geophysical observations. The first chapter introduces an updated time-domain method for computing the viscoelastic Love numbers  – normalized spherical harmonic responses of an Earth with radially symmetric properties. The method employs a novel normalization and coordinate transformation that, when used in combination with the relaxation method for two-point boundary value problems, yields a very effective method of computation that is applicable to a wide range of possible rheological models. The second chapter describes a geometric perspective of GIA modeling using a heuristic example of the sea level response of a single ice cap melting, a prototype of a full inversion of global rheology and deglaciation. By considering the locus of all possible model predictions, a surface called the model manifold, we demonstrate universal features of nonlinear models, such as edges where parameters unphysically go to infinity, and how these can interfere when inferring parameters from data. Applying geometric corrections to the Levenberg-Marquardt least-squares algorithm facilitate finding the best-fit on the model manifold without getting stuck on an edge, even when started from far away. The final chapter employs a different aspect of this perspective, optimal experiment design, to evaluate the geophysical constraints on the configuration and volume of the Barents Sea Ice Sheet over the last glacial cycle and propose maximally constraining observations. Available observations of GIA in the Barents Sea cannot distinguish between a single, large dome and a more moderate amount of ice in the north. Experimental design identifies an area in the central Barents Sea within which a single observation of uplift would be very constraining. Advisors/Committee Members: Sethna, James Patarasp (committeeMember), Franck, Carl Peter (committeeMember).

Subjects/Keywords: Statistics; Glacial isostatic adjustment; Mantle rheology; Relative sea level; Sloppy models; Information geometry; Geographic information science and geodesy; Geophysics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kachuck, S. B. (2018). Time-domain glacial isostatic adjustment: theory, computation, and statistical applications . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/59781

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kachuck, Samuel Benjamin. “Time-domain glacial isostatic adjustment: theory, computation, and statistical applications .” 2018. Thesis, Cornell University. Accessed May 20, 2019. http://hdl.handle.net/1813/59781.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kachuck, Samuel Benjamin. “Time-domain glacial isostatic adjustment: theory, computation, and statistical applications .” 2018. Web. 20 May 2019.

Vancouver:

Kachuck SB. Time-domain glacial isostatic adjustment: theory, computation, and statistical applications . [Internet] [Thesis]. Cornell University; 2018. [cited 2019 May 20]. Available from: http://hdl.handle.net/1813/59781.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kachuck SB. Time-domain glacial isostatic adjustment: theory, computation, and statistical applications . [Thesis]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/59781

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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