Book Section records
https://feeds.library.caltech.edu/people/Owhadi-H/book_section.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 14:05:11 +0000Numerical coarsening of inhomogeneous elastic materials
https://resolver.caltech.edu/CaltechAUTHORS:20161116-134645591
Authors: {'items': [{'id': 'Kharevych-L', 'name': {'family': 'Kharevych', 'given': 'Lily'}}, {'id': 'Mullen-P', 'name': {'family': 'Mullen', 'given': 'Patrick'}}, {'id': 'Owhadi-H', 'name': {'family': 'Owhadi', 'given': 'Houman'}, 'orcid': '0000-0002-5677-1600'}, {'id': 'Desbrun-M', 'name': {'family': 'Desbrun', 'given': 'Mathieu'}, 'orcid': '0000-0003-3424-6079'}]}
Year: 2009
DOI: 10.1145/1576246.1531357
We propose an approach for efficiently simulating elastic objects made of non-homogeneous, non-isotropic materials. Based on recent developments in homogenization theory, a methodology is introduced to approximate a deformable object made of arbitrary fine structures of various linear elastic materials with a dynamicallysimilar coarse model. This numerical coarsening of the material properties allows for simulation of fine, heterogeneous structures on very coarse grids while capturing the proper dynamics of the original dynamical system, thus saving orders of magnitude in computational time. Examples including inhomogeneous and/or anisotropic materials can be realistically simulated in realtime with a numerically-coarsened model made of a few mesh elements.https://authors.library.caltech.edu/records/9xsjf-y3481Multiple target detection using Bayesian learning
https://resolver.caltech.edu/CaltechAUTHORS:20170328-173555191
Authors: {'items': [{'id': 'Nair-S', 'name': {'family': 'Nair', 'given': 'Sujit'}}, {'id': 'Chevva-K-R', 'name': {'family': 'Chevva', 'given': 'Konda Reddy'}}, {'id': 'Owhadi-H', 'name': {'family': 'Owhadi', 'given': 'Houman'}, 'orcid': '0000-0002-5677-1600'}, {'id': 'Marsden-J-E', 'name': {'family': 'Marsden', 'given': 'Jerrold'}}]}
Year: 2009
DOI: 10.1109/CDC.2009.5399565
n this paper, we study multiple target detection using Bayesian learning. The main aim of the paper is to present a computationally efficient way to compute the belief map update exactly and efficiently using results from the theory of symmetric polynomials. In order to illustrate the idea, we consider a simple search scenario with multiple search agents and an unknown but fixed number of stationary targets in a given region that is divided into cells. To estimate the number of targets, a belief map for number of targets is also propagated. The belief map is updated using Bayes' theorem and an optimal reassignment of vehicles based on the values of the current belief map is adopted. Exact computation of the belief map update is combinatorial in nature and often an approximation is needed. We show that the Bayesian update can be exactly computed in an efficient manner using Newton's identities and the detection history in each cell.https://authors.library.caltech.edu/records/y2zbg-efa41Convex optimal uncertainty quantification: Algorithms and a case study in energy storage placement for power grids
https://resolver.caltech.edu/CaltechCDSTR:2012.002
Authors: {'items': [{'id': 'Han-Shuo', 'name': {'family': 'Han', 'given': 'Shuo'}}, {'id': 'Topcu-U', 'name': {'family': 'Topcu', 'given': 'Ufuk'}}, {'id': 'Tao-Molei', 'name': {'family': 'Tao', 'given': 'Molei'}}, {'id': 'Owhadi-H', 'name': {'family': 'Owhadi', 'given': 'Houman'}, 'orcid': '0000-0002-5677-1600'}, {'id': 'Murray-R-M', 'name': {'family': 'Murray', 'given': 'Richard M.'}, 'orcid': '0000-0002-5785-7481'}]}
Year: 2013
How does one evaluate the performance of a stochastic system in the absence of a perfect model (i.e. probability distribution)? We address this question under the framework of optimal uncertainty quantification (OUQ), which is an information-based approach for worst-case analysis of stochastic systems. We are able to generalize previous results and show that the OUQ problem can be solved using convex optimization when the function under evaluation can be expressed in a polytopic canonical form (PCF). We also propose iterative methods for scaling the convex formulation to larger systems. As an application, we study the problem of storage placement in power grids with renewable generation. Numerical simulation results for simple artificial examples as well as an example using the IEEE 14-bus test case with real wind generation data are presented to demonstrate the usage of OUQ analysis.https://authors.library.caltech.edu/records/d30r8-zpb47Modeling Across Scales: Discrete Geometric Structures in Homogenization and Inverse Homogenization
https://resolver.caltech.edu/CaltechAUTHORS:20210224-145143008
Authors: {'items': [{'id': 'Desbrun-M', 'name': {'family': 'Desbrun', 'given': 'Mathieu'}, 'orcid': '0000-0003-3424-6079'}, {'id': 'Donaldson-Roger-D', 'name': {'family': 'Donaldson', 'given': 'Roger D.'}}, {'id': 'Owhadi-H', 'name': {'family': 'Owhadi', 'given': 'Houman'}, 'orcid': '0000-0002-5677-1600'}]}
Year: 2013
DOI: 10.1002/9783527671632.ch02
Imaging and simulation methods are typically constrained to resolutions much coarser than the scale of physical microstructures present in body tissues or geological features. Mathematical homogenization and numerical homogenization address this practical issue by identifying and computing appropriate spatial averages that result in accuracy and consistency between the macroscales we observe and the underlying microscale models we assume. Among the various applications benefiting from homogenization, electrical impedance tomography (EIT) images the electrical conductivity of a body by measuring electrical potentials consequential to electric currents applied to the exterior of the body. EIT is routinely used in breast cancer detection and cardiopulmonary imaging, where current flow in fine‐scale tissues underlies the resulting coarse‐scale images.https://authors.library.caltech.edu/records/51bza-ncp54Introduction to Uncertainty Quantification
https://resolver.caltech.edu/CaltechAUTHORS:20170615-123732747
Authors: {'items': [{'id': 'Ghanem-R', 'name': {'family': 'Ghanem', 'given': 'Roger'}}, {'id': 'Higdon-D', 'name': {'family': 'Higdon', 'given': 'David'}}, {'id': 'Owhadi-H', 'name': {'family': 'Owhadi', 'given': 'Houman'}, 'orcid': '0000-0002-5677-1600'}]}
Year: 2016
DOI: 10.1007/978-3-319-11259-6_1-1
Technology, in common with many other activities, tends toward avoidance of risks by investors. Uncertainty is ruled out if possible. People generally prefer the predictable. Few recognize how destructive this can be, how it imposes severe limits on variability and thus makes whole populations fatally vulnerable to the shocking ways our universe can throw the dice.https://authors.library.caltech.edu/records/mzk6w-jev07Self-Powered and Bio-Inspired Dynamic Systems: Research and Education
https://resolver.caltech.edu/CaltechAUTHORS:20170602-084400992
Authors: {'items': [{'id': 'Khoshnoud-F', 'name': {'family': 'Khoshnoud', 'given': 'Farbod'}}, {'id': 'Esat-I-I', 'name': {'family': 'Esat', 'given': 'Ibrahim I.'}}, {'id': 'Bonser-R-H-C', 'name': {'family': 'Bonser', 'given': 'Richard H. C.'}}, {'id': 'de-Silva-C-W', 'name': {'family': 'de Silva', 'given': 'Clarence W.'}}, {'id': 'McKerns-M-M', 'name': {'family': 'McKerns', 'given': 'Michael M.'}}, {'id': 'Owhadi-H', 'name': {'family': 'Owhadi', 'given': 'Houman'}, 'orcid': '0000-0002-5677-1600'}]}
Year: 2016
DOI: 10.1115/IMECE2016-65276
Animals are products of nature and have evolved over millions of years to perform better in their activities. Engineering research and development can benefit greatly by looking into nature and finding engineering solutions by learning from animals' evolution and biological systems. Another relevant factor in the present context is highlighted by the statement of the Nobel laureate Richard Smalley: "Energy is the single most important problem facing humanity today." This paper focuses on how the research and education in the area of Dynamic Systems can be geared towards these two considerations. In particular, recent advances in self-powered dynamic systems and bio-inspired dynamic systems are highlighted. Self-powered dynamic systems benefit by capturing wasted energy in a dynamic system and converting it into useful energy in the mode of a regenerative system, possibly in conjunction with renewable energies. Examples of solar-powered vehicles, regenerative vibration control, and energy harvesting are presented in the paper. Particularly, development of solar-powered quadrotor, octocopter, and tricopter airships are presented, a self-powered vibration control of a mass-spring system using electromagnetic actuators/generators, and piezoelectric flutter energy harvesting using bi-stable material are discussed. As examples of bioinspired dynamic systems, flapping wing flying robots, vertical axis wind turbines inspired by fish schooling, propulsion inspired by jellyfish, and Psi Intelligent Control are given. In particular, various design and developments of bird-inspired and insect-inspired flapping wings with the piezoelectric and electromagnetic actuation mechanisms, a scaled vertical axis wind turbine farm consist of 4 turbines and the corresponding wind tunnel testing, jellyfish-inspired pulsing jet and experimenting the increase in efficiency of energy consumption, and a multi-agent/robotic based predictive control scheme inspired by Psi precognition (event or state not yet experienced). Examples of student projects and research carried out at Brunel University and the experimental rigs built (in all the mentioned areas) are discussed, as an integrated research and educational activity. For the analysis and understanding of the behavior of self-powered and bio-inspired systems, Optimal Uncertainty Quantification (OUQ) is used. OUQ establishes a unified analysis framework in obtaining optimized solutions of the dynamic systems responses, which takes into account uncertainties and incomplete information in the simulation of these systems.https://authors.library.caltech.edu/records/48shk-zx068The game theoretic approach to Uncertainty Quantification, reduced order modeling and numerical analysis
https://resolver.caltech.edu/CaltechAUTHORS:20190816-144341264
Authors: {'items': [{'id': 'Owhadi-H', 'name': {'family': 'Owhadi', 'given': 'Houman'}, 'orcid': '0000-0002-5677-1600'}]}
Year: 2017
DOI: 10.2514/6.2017-1092
We discuss the development of Uncertainty Quantification framework founded upon a combination of game/decision theory and information based complexity. We suggest that such a framework could be used not only to guide decisions in presence of epistemic uncertainties and complexity management capabilities constraints but also to automate the process of discovery in (1) model form uncertainty quantification and design (2) model reduction (3) the design of fast, robust and scalable numerical solvers. Although these applications appear dissimilar, they are all based on the efficient processing of incomplete information with limited computational resources: (1) model form UQ and design require the management and processing of epistemic uncertainties and limited data (2) model reduction requires the approximation of the full state of a complex system through operations performed on a few (coarse/reduced) variables (3) fast and robust computation requires computation with partial information. The core idea of the proposed framework is to reformulate the process of computing with partial information and limited resources as that of playing underlying hierarchies of adversarial information games characterizing the adversarial and nested processing of hierarchies of partial/missing information.https://authors.library.caltech.edu/records/nc3pj-2xh45Toward Machine Wald
https://resolver.caltech.edu/CaltechAUTHORS:20160224-064915023
Authors: {'items': [{'id': 'Owhadi-H', 'name': {'family': 'Owhadi', 'given': 'Houman'}, 'orcid': '0000-0002-5677-1600'}, {'id': 'Scovel-C', 'name': {'family': 'Scovel', 'given': 'Clint'}, 'orcid': '0000-0001-7757-3411'}]}
Year: 2017
DOI: 10.1007/978-3-319-12385-1_3
The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed by humans because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to think as humans, especially when faced with uncertainty, is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well-posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tends to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with decision theory, machine learning, Bayesian inference, stochastic optimization, robust optimization, optimal uncertainty quantification, and information-based complexity.https://authors.library.caltech.edu/records/3zem5-1gn17