PPT-INCLUDING UNCERTAINTY MODELS FOR

SURROGATE BASED GLOBAL DESIGN OPTIMIZATION The EGO algorithm STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION GROUP Thanks to Felipe A C Viana 2 BACKGROUND SURROGATE MODELING Differences are larger in regions of low point density. SSA. through Orbit Determination of Two-Line Element Sets. David . A. Vallado, Benjamin . Bastida. . Virgili. , and Tim . Flohrer. Paper 6ECSD 13-4a.0-7 presented . at the . 6. th. European Conference on Space Debris . Overview. Intro: SATIM. UNEP . Project – SATIM-MC. MAPS Project – . SATIM-SP. ERC’s Bread n Butter Model: . SATIM (South African TIMES Model). Deterministic Least Cost Planning Model. (Similar to Model used for IRP/IEP). for S2D forecasting. EUPORIAS wp31. Nov 2012, Ronald Hutjes. Background. S2D impact prediction. Uncertainty explosion / Skill implosion ??. SST. Weather. (Downscaling). Soil moisture. Plant productivity. An Evaluation of Development Viability. Peter Byrne. Pat McAllister. Peter Wyatt. www.henley.reading.ac.uk/rep/fulltxt/0810.pdf. Structure. Main Research questions. Background and context. Current practice. Stanford University, USA. A strategy for managing uncertainty. Importance of uncertainty and risk. New well planned. P1. P2. P3. P4. West-Coast Africa (WCA) slope-valley system. Data courtesy of Chevron. Introduction to Random Dynamical Systems. Mrinal Kumar. Assistant Prof., MAE. http://. www.mae.ufl.edu. /~. mrinalkumar. Syllabus…. Uncertainty: A Fundamental Challenge. Nature is far too complex for engineers. environmental research. Liew Xuan Qi (A0157765N). Cheong Hui Ping (A0127945W). Hong Chuan Yin (A0155305M). Best Practice Approaches for Characterizing, Communicating, and Incorporating Scientific Uncertainty in Climate Decision Making. The EGO algorithm. 1. Introduction to optimization with surrogates. Based on cycles. Each consists of sampling design points by simulations, fitting surrogates to simulations and then optimizing an objective.. Objective. The Los Alamos Sea Ice model has a number of input parameters for which accurate values are not always well established. . We conduct a variance-based sensitivity analysis . of hemispheric sea ice properties to 39 input parameters. The method accounts for non-linear and non-additive effects in the model.. Turing Fellow at the Alan Turing Institute. Thanks to Amy Wilson, Stan Zachary, Michael Goldstein – and too many others to name. Statistical modelling for planning and policy applications. It matters!. Model probabilities with beta distributions based on observed frequencies, where variance corresponse to epistemic uncertaintyThompson sampling for selecting queries (balancing exploration-exploitatio In order to evaluate the plant safety performance, acceptance criteria are properly selected according to estabilished international practice. The two main aspects which have been considered for devel 1. ERiMA. : . Envisioning Risk Models for Assessment of AI-based applications.. 2. Dr Huma Samin. 1. Post Doctoral Research Associate Computer Science. Durham University, UK. huma.samin@durham.ac.uk. An Evaluation of Development Viability. Peter Byrne. Pat McAllister. Peter Wyatt. www.henley.reading.ac.uk/rep/fulltxt/0810.pdf. Structure. Main Research questions. Background and context. Current practice.