Prof. William S. Oates
Professor at Florida State Univ
SPIE Involvement:
Conference Program Committee | Author | Instructor
Publications (36)

Proceedings Article | 22 April 2020 Presentation + Paper
Proc. SPIE. 11377, Behavior and Mechanics of Multifunctional Materials IX
KEYWORDS: Mathematical modeling, Statistical analysis, Foam, Data modeling, Mechanics, Calibration, Calculus, Performance modeling, Thermodynamics, Absorption

Proceedings Article | 29 March 2019 Paper
Proc. SPIE. 10968, Behavior and Mechanics of Multifunctional Materials XIII
KEYWORDS: Mathematical modeling, Data modeling, Calibration, Calculus, Mechanical engineering

Proceedings Article | 22 March 2018 Presentation + Paper
Proc. SPIE. 10596, Behavior and Mechanics of Multifunctional Materials and Composites XII
KEYWORDS: Ferroelectric materials, Statistical analysis, Nano opto mechanical systems, Data modeling, Polarization, Calibration, Differential equations, Systems modeling, Lead, Bayesian inference

Proceedings Article | 22 March 2018 Presentation + Paper
Proc. SPIE. 10596, Behavior and Mechanics of Multifunctional Materials and Composites XII
KEYWORDS: Statistical analysis, Foam, Data modeling, Calibration, Error analysis, Dielectrics, Calculus, Analytical research, Statistical modeling, Thermodynamics

Proceedings Article | 17 April 2017 Presentation + Paper
Proc. SPIE. 10163, Electroactive Polymer Actuators and Devices (EAPAD) 2017
KEYWORDS: Actuators, Data modeling, Mechanics, Sensors, Calibration, Polymers, Dielectrics, Robotics, Computer simulations, Robots, Structural analysis, Robotic systems, Systems modeling, Thermodynamics

Showing 5 of 36 publications
Conference Committee Involvement (4)
Behavior and Mechanics of Multifunctional Materials XV
8 March 2021 | Long Beach, California, United States
Behavior and Mechanics of Multifunctional Materials XIV
27 April 2020 | Online Only, California, United States
Behavior and Mechanics of Multifunctional Materials XIII
4 March 2019 | Denver, Colorado, United States
Behavior and Mechanics of Multifunctional Materials and Composites XII
5 March 2018 | Denver, Colorado, United States
Course Instructor
SC1188: Applications of Uncertainty Quantification and Sensitivity Analysis in Smart Materials and Adaptive Structures
The purpose of this hands-on tutorial is to expose participants to statistical and numerical techniques that will allow them to quantify the accuracy of multi-physics models and simulation codes for active materials and structures when one accounts for uncertainty or errors in models, parameters, numerical simulation codes, and data. Additionally, we will discuss global sensitivity analysis techniques for parameters, as well as uncertainty propagation techniques, and illustrate how they provide insights regarding material behavior and can be used to quantify the accuracy of predictions.<br/> In the first part of the tutorial, we will provide an overview of Bayesian statistics, sensitivity analysis methodologies, and numerical algorithms necessary to propagate input uncertainties through simulation codes. We will consider several case studies to illustrate these techniques for a variety of materials and smart structure applications. These include models for piezoelectric macro-fiber composites, shape memory alloys, viscoelastic polymers, graphene thermoacoustics, quantum-informed ferroelectric continuum models, and Rietveld analysis. In this part of the tutorial, we will provide participants with algorithms that quantify the uncertainties in model parameters, such as piezoelectric constants, when they are calibrated from experimental data. We will show how global sensitivity analysis can be used to rank model parameters and isolate those parameters that cannot be reliably estimated from data. To illustrate the uncertainty propagation techniques, we will demonstrate the construction of 95% prediction intervals for PZT models at a given applied field. Finally, we will demonstrate, in the context of a shape memory alloy example, the manner in which robust control designs can be improved through uncertainty quantification.<br/> In the second, hands-on, part of the tutorial, we will have participants run case studies using MATLAB. These studies will include models and data provided by the instructors, but participants are also encouraged to bring their own models and data for testing during the tutorial, based on their specific problem(s) of interest.
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