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<div class="elementToProof ContentPasted0"><span style="font-size: 12pt;"><b>"Identify Important and Influential Processes of Complex Environmental Systems under Model and Parametric Uncertainty" </b></span>
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<div class="FluidPluginCopy ContentPasted0 elementToProof"><span style="font-size: 14pt;"><b>Ming Ye </b></span></div>
<div class="FluidPluginCopy ContentPasted0 elementToProof">Department of Earth, Ocean, and Atmospheric Science (EOAS)<br>
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<div class="FluidPluginCopy ContentPasted0 elementToProof">Department of Scientific Computing<br>
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<div class="FluidPluginCopy ContentPasted0">Florida State University</div>
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<div class="FluidPluginCopy ContentPasted0 elementToProof">NOTE: Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic.
<b>All are welcome to attend. </b></div>
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<div class="FluidPluginCopy ContentPasted0 elementToProof"><b>https://fsu.zoom.us/j/94273595552 </b></div>
<div class="FluidPluginCopy ContentPasted0 elementToProof">Meeting # <b>942 7359 5552 </b></div>
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<div class="FluidPluginCopy ContentPasted0 elementToProof"><b>Wednesday, Jan 11th</b>, 2023, Schedule: </div>
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<div class="FluidPluginCopy ContentPasted0 elementToProof">* 3:00 to 3:30 PM Eastern Time (US and Canada) </div>
<div class="FluidPluginCopy ContentPasted0 elementToProof">Nespresso & Teatime (in 417 DSL Commons) </div>
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<div class="FluidPluginCopy ContentPasted0 elementToProof">* <b>3:30 to 4:30 PM</b> Eastern Time (US and Canada) </div>
<div class="FluidPluginCopy ContentPasted0 elementToProof"><b>Colloquium</b> - Attend F2F (in 499 DSL) or Virtually (via Zoom)
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<div class="FluidPluginCopy ContentPasted0 elementToProof"><b>Abstract: </b></div>
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<div class="elementToProof ContentPasted0">Sensitivity analysis is a vital tool in the modeling community to identify important and influential parameters for model development and improvement, and variance-based global sensitivity analysis has gained popularity.
However, the conventional global sensitivity indices are defined with consideration of only parametric uncertainty, but not model uncertainty that arises when a system’s process can be represented by multiple conceptual-mathematical models. Multi-model sensitivity
analysis has gained increasing attention for advancing our understanding of complex Earth and environmental systems with interacting physical, chemical, and biological processes. Based on a hierarchical structure of parameter and model uncertainties and on
recently developed techniques of model averaging, we developed two new process sensitivity indices for identifying important and influential processes. The indices are designed to answer the following question: how can we identify important and influential
processes for the explicitly proposed process models and the probabilistically defined random parameters? A computationally efficient algorithm was also developed to reduce computational cost for the indices. To further reduce computational cost, we developed
a new global sensitivity analysis method, called multi-model difference-based sensitivity analysis (MMDS), which can screen noninfluential system process from further investigation such as model calibration. In this seminar, I will present the three methods
in a context of environmental modeling with numerical implementation and evaluation. The methods are mathematically and computationally general, and can be applied to a wide range of problems of numerical modeling.<span style="font-family: Tahoma, Geneva, sans-serif; font-size: 12pt; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);"><br>
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