1\chapter{Optimization Under Uncertainty (OUU)}\label{ouu} 2 3\section{Reliability-Based Design Optimization (RBDO)}\label{ouu:rbdo} 4 5Reliability-based design optimization (RBDO) methods are used to 6perform design optimization accounting for reliability metrics. The 7reliability analysis capabilities described in 8Section~\ref{uq:reliability:local} provide a substantial foundation 9for exploring a variety of gradient-based RBDO formulations. 10\cite{Eld05} investigated bi-level, fully-analytic bi-level, and 11first-order sequential RBDO approaches employing underlying 12first-order reliability assessments. \cite{Eld06a} investigated 13fully-analytic bi-level and second-order sequential RBDO approaches 14employing underlying second-order reliability assessments. These 15methods are overviewed in the following sections. 16 17\subsection{Bi-level RBDO} \label{ouu:rbdo:bilev} 18 19The simplest and most direct RBDO approach is the bi-level approach in 20which a full reliability analysis is performed for every optimization 21function evaluation. This involves a nesting of two distinct levels 22of optimization within each other, one at the design level and one at 23the MPP search level. 24 25Since an RBDO problem will typically specify both the $\bar{z}$ level 26and the $\bar{p}/\bar{\beta}$ level, one can use either the RIA or the 27PMA formulation for the UQ portion and then constrain the result in 28the design optimization portion. In particular, RIA reliability 29analysis maps $\bar{z}$ to $p/\beta$, so RIA RBDO constrains $p/\beta$: 30\begin{eqnarray} 31 {\rm minimize } & f \nonumber \\ 32 {\rm subject \ to } & \beta \ge \bar{\beta} \nonumber \\ 33 {\rm or } & p \le \bar{p} \label{eq:rbdo_ria} 34\end{eqnarray} 35 36\noindent And PMA reliability analysis maps $\bar{p}/\bar{\beta}$ to 37$z$, so PMA RBDO constrains $z$: 38\begin{eqnarray} 39 {\rm minimize } & f \nonumber \\ 40 {\rm subject \ to } & z \ge \bar{z} \label{eq:rbdo_pma} 41\end{eqnarray} 42 43\noindent where $z \ge \bar{z}$ is used as the RBDO constraint for 44a cumulative failure probability (failure defined as $z \le \bar{z}$) 45but $z \le \bar{z}$ would be used as the RBDO constraint for a 46complementary cumulative failure probability (failure defined as $z 47\ge \bar{z}$). It is worth noting that Dakota is not limited to these 48types of inequality-constrained RBDO formulations; rather, they are 49convenient examples. Dakota supports general optimization under 50uncertainty mappings~\cite{Eld02} which allow flexible use of 51statistics within multiple objectives, inequality constraints, and 52equality constraints. 53 54An important performance enhancement for bi-level methods is the use 55of sensitivity analysis to analytically compute the design gradients 56of probability, reliability, and response levels. When design 57variables are separate from the uncertain variables (i.e., they are 58not distribution parameters), then the following first-order 59expressions may be used~\cite{Hoh86,Kar92,All04}: 60\begin{eqnarray} 61\nabla_{\bf d} z & = & \nabla_{\bf d} g \label{eq:deriv_z} \\ 62\nabla_{\bf d} \beta_{cdf} & = & \frac{1}{{\parallel \nabla_{\bf u} G 63\parallel}} \nabla_{\bf d} g \label{eq:deriv_beta} \\ 64\nabla_{\bf d} p_{cdf} & = & -\phi(-\beta_{cdf}) \nabla_{\bf d} \beta_{cdf} 65\label{eq:deriv_p} 66\end{eqnarray} 67where it is evident from Eqs.~\ref{eq:beta_cdf_ccdf}-\ref{eq:p_cdf_ccdf} 68that $\nabla_{\bf d} \beta_{ccdf} = -\nabla_{\bf d} \beta_{cdf}$ and 69$\nabla_{\bf d} p_{ccdf} = -\nabla_{\bf d} p_{cdf}$. In the case of 70second-order integrations, Eq.~\ref{eq:deriv_p} must be expanded to 71include the curvature correction. For Breitung's correction 72(Eq.~\ref{eq:p_2nd_breit}), 73\begin{equation} 74\nabla_{\bf d} p_{cdf} = \left[ \Phi(-\beta_p) \sum_{i=1}^{n-1} 75\left( \frac{-\kappa_i}{2 (1 + \beta_p \kappa_i)^{\frac{3}{2}}} 76\prod_{\stackrel{\scriptstyle j=1}{j \ne i}}^{n-1} 77\frac{1}{\sqrt{1 + \beta_p \kappa_j}} \right) - 78\phi(-\beta_p) \prod_{i=1}^{n-1} \frac{1}{\sqrt{1 + \beta_p \kappa_i}} 79\right] \nabla_{\bf d} \beta_{cdf} \label{eq:deriv_p_breit} 80\end{equation} 81where $\nabla_{\bf d} \kappa_i$ has been neglected and $\beta_p \ge 0$ 82(see Section~\ref{uq:reliability:local:mpp:int}). Other approaches assume 83the curvature correction is nearly independent of the design 84variables~\cite{Rac02}, which is equivalent to neglecting the first 85term in Eq.~\ref{eq:deriv_p_breit}. 86 87To capture second-order probability estimates within an RIA RBDO 88formulation using well-behaved $\beta$ constraints, a generalized 89reliability index can be introduced where, similar to Eq.~\ref{eq:beta_cdf}, 90\begin{equation} 91\beta^*_{cdf} = -\Phi^{-1}(p_{cdf}) \label{eq:gen_beta} 92\end{equation} 93for second-order $p_{cdf}$. This reliability index is no longer 94equivalent to the magnitude of ${\bf u}$, but rather is a convenience 95metric for capturing the effect of more accurate probability 96estimates. The corresponding generalized reliability index 97sensitivity, similar to Eq.~\ref{eq:deriv_p}, is 98\begin{equation} 99\nabla_{\bf d} \beta^*_{cdf} = -\frac{1}{\phi(-\beta^*_{cdf})} 100\nabla_{\bf d} p_{cdf} \label{eq:deriv_gen_beta} 101\end{equation} 102where $\nabla_{\bf d} p_{cdf}$ is defined from Eq.~\ref{eq:deriv_p_breit}. 103Even when $\nabla_{\bf d} g$ is estimated numerically, 104Eqs.~\ref{eq:deriv_z}-\ref{eq:deriv_gen_beta} can be used to avoid 105numerical differencing across full reliability analyses. 106 107When the design variables are distribution parameters of the uncertain 108variables, $\nabla_{\bf d} g$ is expanded with the chain rule and 109Eqs.~\ref{eq:deriv_z} and~\ref{eq:deriv_beta} become 110\begin{eqnarray} 111\nabla_{\bf d} z & = & \nabla_{\bf d} {\bf x} \nabla_{\bf x} g 112\label{eq:deriv_z_ds} \\ 113\nabla_{\bf d} \beta_{cdf} & = & \frac{1}{{\parallel \nabla_{\bf u} G 114\parallel}} \nabla_{\bf d} {\bf x} \nabla_{\bf x} g \label{eq:deriv_beta_ds} 115\end{eqnarray} 116where the design Jacobian of the transformation ($\nabla_{\bf d} {\bf x}$) 117may be obtained analytically for uncorrelated ${\bf x}$ or 118semi-analytically for correlated ${\bf x}$ ($\nabla_{\bf d} {\bf L}$ 119is evaluated numerically) by differentiating Eqs.~\ref{eq:trans_zx} 120and~\ref{eq:trans_zu} with respect to the distribution parameters. 121Eqs.~\ref{eq:deriv_p}-\ref{eq:deriv_gen_beta} remain the same as 122before. For this design variable case, all required information for 123the sensitivities is available from the MPP search. 124 125Since Eqs.~\ref{eq:deriv_z}-\ref{eq:deriv_beta_ds} are derived using 126the Karush-Kuhn-Tucker optimality conditions for a converged MPP, they 127are appropriate for RBDO using AMV+, AMV$^2$+, TANA, FORM, and SORM, 128but not for RBDO using MVFOSM, MVSOSM, AMV, or AMV$^2$. 129 130 131\subsection{Sequential/Surrogate-based RBDO} \label{ouu:rbdo:surr} 132 133An alternative RBDO approach is the sequential approach, in which 134additional efficiency is sought through breaking the nested 135relationship of the MPP and design searches. The general concept is 136to iterate between optimization and uncertainty quantification, 137updating the optimization goals based on the most recent probabilistic 138assessment results. This update may be based on safety 139factors~\cite{Wu01} or other approximations~\cite{Du04}. 140 141A particularly effective approach for updating the optimization goals 142is to use the $p/\beta/z$ sensitivity analysis of 143Eqs.~\ref{eq:deriv_z}-\ref{eq:deriv_beta_ds} in combination with local 144surrogate models~\cite{Zou04}. In \cite{Eld05} and~\cite{Eld06a}, 145first-order and second-order Taylor series approximations were 146employed within a trust-region model management framework~\cite{Giu00} 147in order to adaptively manage the extent of the approximations and 148ensure convergence of the RBDO process. Surrogate models were used 149for both the objective function and the constraints, although the use 150of constraint surrogates alone is sufficient to remove the nesting. 151 152In particular, RIA trust-region surrogate-based RBDO employs surrogate 153models of $f$ and $p/\beta$ within a trust region $\Delta^k$ centered 154at ${\bf d}_c$. For first-order surrogates: 155\begin{eqnarray} 156 {\rm minimize } & f({\bf d}_c) + \nabla_d f({\bf d}_c)^T 157({\bf d} - {\bf d}_c) \nonumber \\ 158 {\rm subject \ to } & \beta({\bf d}_c) + \nabla_d \beta({\bf d}_c)^T 159({\bf d} - {\bf d}_c) \ge \bar{\beta} \nonumber \\ 160 {\rm or } & p ({\bf d}_c) + \nabla_d p({\bf d}_c)^T 161({\bf d} - {\bf d}_c) \le \bar{p} \nonumber \\ 162& {\parallel {\bf d} - {\bf d}_c \parallel}_\infty \le \Delta^k 163\label{eq:rbdo_surr1_ria} 164\end{eqnarray} 165and for second-order surrogates: 166\begin{eqnarray} 167 {\rm minimize } & f({\bf d}_c) + \nabla_{\bf d} f({\bf d}_c)^T 168({\bf d} - {\bf d}_c) + \frac{1}{2} ({\bf d} - {\bf d}_c)^T 169\nabla^2_{\bf d} f({\bf d}_c) ({\bf d} - {\bf d}_c) \nonumber \\ 170 {\rm subject \ to } & \beta({\bf d}_c) + \nabla_{\bf d} \beta({\bf d}_c)^T 171({\bf d} - {\bf d}_c) + \frac{1}{2} ({\bf d} - {\bf d}_c)^T 172\nabla^2_{\bf d} \beta({\bf d}_c) ({\bf d} - {\bf d}_c) \ge \bar{\beta} 173\nonumber \\ 174 {\rm or } & p ({\bf d}_c) + \nabla_{\bf d} p({\bf d}_c)^T 175({\bf d} - {\bf d}_c) + \frac{1}{2} ({\bf d} - {\bf d}_c)^T 176\nabla^2_{\bf d} p({\bf d}_c) ({\bf d} - {\bf d}_c) \le \bar{p} \nonumber \\ 177& {\parallel {\bf d} - {\bf d}_c \parallel}_\infty \le \Delta^k 178\label{eq:rbdo_surr2_ria} 179\end{eqnarray} 180For PMA trust-region surrogate-based RBDO, surrogate models of 181$f$ and $z$ are employed within a trust region $\Delta^k$ centered 182at ${\bf d}_c$. For first-order surrogates: 183\begin{eqnarray} 184 {\rm minimize } & f({\bf d}_c) + \nabla_d f({\bf d}_c)^T 185({\bf d} - {\bf d}_c) \nonumber \\ 186 {\rm subject \ to } & z({\bf d}_c) + \nabla_d z({\bf d}_c)^T ({\bf d} - {\bf d}_c) 187\ge \bar{z} \nonumber \\ 188& {\parallel {\bf d} - {\bf d}_c \parallel}_\infty \le \Delta^k 189\label{eq:rbdo_surr1_pma} 190\end{eqnarray} 191and for second-order surrogates: 192\begin{eqnarray} 193 {\rm minimize } & f({\bf d}_c) + \nabla_{\bf d} f({\bf d}_c)^T 194({\bf d} - {\bf d}_c) + \frac{1}{2} ({\bf d} - {\bf d}_c)^T 195\nabla^2_{\bf d} f({\bf d}_c) ({\bf d} - {\bf d}_c) \nonumber \\ 196 {\rm subject \ to } & z({\bf d}_c) + \nabla_{\bf d} z({\bf d}_c)^T ({\bf d} - {\bf d}_c) 197 + \frac{1}{2} ({\bf d} - {\bf d}_c)^T \nabla^2_{\bf d} z({\bf d}_c) 198({\bf d} - {\bf d}_c) \ge \bar{z} \nonumber \\ 199& {\parallel {\bf d} - {\bf d}_c \parallel}_\infty \le \Delta^k 200\label{eq:rbdo_surr2_pma} 201\end{eqnarray} 202where the sense of the $z$ constraint may vary as described 203previously. The second-order information in 204Eqs.~\ref{eq:rbdo_surr2_ria} and \ref{eq:rbdo_surr2_pma} will 205typically be approximated with quasi-Newton updates. 206 207 208\section{Stochastic Expansion-Based Design Optimization (SEBDO)} \label{ouu:sebdo} 209 210 211\subsection{Stochastic Sensitivity Analysis} \label{ouu:sebdo:ssa} 212 213Section~\ref{uq:expansion:rvsa} describes sensitivity analysis of the 214polynomial chaos expansion with respect to the expansion variables. 215Here we extend this analysis to include sensitivity analysis of 216probabilistic moments with respect to nonprobabilistic (i.e., design 217or epistemic uncertain) variables. 218 219\subsubsection{Local sensitivity analysis: first-order probabilistic expansions} \label{ouu:sebdo:ssa:dvsa_rve} 220 221With the introduction of nonprobabilistic variables $\boldsymbol{s}$ 222(for example, design variables or epistemic uncertain variables), a 223polynomial chaos expansion only over the probabilistic variables 224$\boldsymbol{\xi}$ has the functional relationship: 225\begin{equation} 226R(\boldsymbol{\xi}, \boldsymbol{s}) \cong \sum_{j=0}^P \alpha_j(\boldsymbol{s}) 227\Psi_j(\boldsymbol{\xi}) \label{eq:R_alpha_s_psi_xi} 228\end{equation} 229 230\noindent For computing sensitivities of response mean and variance, 231the $ij$ indices may be dropped from Eqs.~\ref{eq:mean_pce} 232and~\ref{eq:covar_pce}, simplifying to 233\begin{equation} 234\mu(s) ~=~ \alpha_0(s), ~~~~\sigma^2(s) = \sum_{k=1}^P \alpha^2_k(s) \langle \Psi^2_k \rangle \label{eq:var_pce} 235\end{equation} 236Sensitivities of Eq.~\ref{eq:var_pce} with 237respect to the nonprobabilistic variables are as follows, where 238independence of $\boldsymbol{s}$ and $\boldsymbol{\xi}$ is assumed: 239\begin{eqnarray} 240\frac{d\mu}{ds} &=& \frac{d\alpha_0}{ds} ~~=~~ 241%\frac{d}{ds} \langle R \rangle ~~=~~ 242\langle \frac{dR}{ds} \rangle \label{eq:dmuR_ds_xi_pce} \\ 243\frac{d\sigma^2}{ds} &=& \sum_{k=1}^P \langle \Psi_k^2 \rangle 244\frac{d\alpha_k^2}{ds} ~~=~~ 2452 \sum_{k=1}^P \alpha_k \langle \frac{dR}{ds}, \Psi_k \rangle 246\label{eq:dsigR_ds_xi_pce} 247%2 \sigma \frac{d\sigma}{ds} &=& 2 248%\sum_{k=1}^P \alpha_k \frac{d\alpha_k}{ds} \langle \Psi_k^2 \rangle \\ 249%\frac{d\sigma}{ds} &=& \frac{1}{\sigma} 250%\sum_{k=1}^P \alpha_k \frac{d}{ds} \langle R, \Psi_k \rangle 251%\label{eq:dsigR_ds_xi_pce} 252\end{eqnarray} 253where 254\begin{equation} 255\frac{d\alpha_k}{ds} = \frac{\langle \frac{dR}{ds}, \Psi_k \rangle} 256{\langle \Psi^2_k \rangle} \label{eq:dalpha_k_ds} 257\end{equation} 258has been used. Due to independence, the coefficients calculated in 259Eq.~\ref{eq:dalpha_k_ds} may be interpreted as either the derivatives 260of the expectations or the expectations of the derivatives, or more 261precisely, the nonprobabilistic sensitivities of the chaos 262coefficients for the response expansion or the chaos coefficients of 263an expansion for the nonprobabilistic sensitivities of the response. 264The evaluation of integrals involving $\frac{dR}{ds}$ extends the data 265requirements for the PCE approach to include response sensitivities at 266each of the sampled points.% for the quadrature, sparse grid, sampling, 267%or point collocation coefficient estimation approaches. 268The resulting expansions are valid only for a particular set of 269nonprobabilistic variables and must be recalculated each time the 270nonprobabilistic variables are modified. 271 272Similarly for stochastic collocation, 273\begin{equation} 274R(\boldsymbol{\xi}, \boldsymbol{s}) \cong \sum_{k=1}^{N_p} r_k(\boldsymbol{s}) 275\boldsymbol{L}_k(\boldsymbol{\xi}) \label{eq:R_r_s_L_xi} 276\end{equation} 277leads to 278\begin{eqnarray} 279\mu(s) &=& \sum_{k=1}^{N_p} r_k(s) w_k, ~~~~\sigma^2(s) ~=~ \sum_{k=1}^{N_p} r^2_k(s) w_k - \mu^2(s) \label{eq:var_sc} \\ 280\frac{d\mu}{ds} &=& %\frac{d}{ds} \langle R \rangle ~~=~~ 281%\sum_{k=1}^{N_p} \frac{dr_k}{ds} \langle \boldsymbol{L}_k \rangle ~~=~~ 282\sum_{k=1}^{N_p} w_k \frac{dr_k}{ds} \label{eq:dmuR_ds_xi_sc} \\ 283\frac{d\sigma^2}{ds} &=& \sum_{k=1}^{N_p} 2 w_k r_k \frac{dr_k}{ds} 284- 2 \mu \frac{d\mu}{ds} 285~~=~~ \sum_{k=1}^{N_p} 2 w_k (r_k - \mu) \frac{dr_k}{ds} 286\label{eq:dsigR_ds_xi_sc} 287\end{eqnarray} 288%based on differentiation of Eqs.~\ref{eq:mean_sc}-\ref{eq:covar_sc}. 289 290\subsubsection{Local sensitivity analysis: zeroth-order combined expansions} \label{ouu:sebdo:ssa:dvsa_cve} 291 292Alternatively, a stochastic expansion can be formed over both 293$\boldsymbol{\xi}$ and $\boldsymbol{s}$. Assuming a bounded 294design domain $\boldsymbol{s}_L \le \boldsymbol{s} \le 295\boldsymbol{s}_U$ (with no implied probability content), a Legendre 296chaos basis would be appropriate for each of the dimensions in 297$\boldsymbol{s}$ within a polynomial chaos expansion. 298\begin{equation} 299R(\boldsymbol{\xi}, \boldsymbol{s}) \cong \sum_{j=0}^P \alpha_j 300\Psi_j(\boldsymbol{\xi}, \boldsymbol{s}) \label{eq:R_alpha_psi_xi_s} 301\end{equation} 302 303\noindent In this case, design sensitivities for the mean and variance 304do not require response sensitivity data, but this comes at the cost 305of forming the PCE over additional dimensions. For this combined 306variable expansion, the mean and variance are evaluated by performing 307the expectations over only the probabilistic expansion variables, 308which eliminates the polynomial dependence on $\boldsymbol{\xi}$, 309leaving behind the desired polynomial dependence of the moments on 310$\boldsymbol{s}$: 311\begin{eqnarray} 312\mu_R(\boldsymbol{s}) &=& \sum_{j=0}^P \alpha_j \langle \Psi_j(\boldsymbol{\xi}, 313\boldsymbol{s}) \rangle_{\boldsymbol{\xi}} \label{eq:muR_comb_pce} \\ 314\sigma^2_R(\boldsymbol{s}) &=& \sum_{j=0}^P \sum_{k=0}^P \alpha_j \alpha_k 315\langle \Psi_j(\boldsymbol{\xi}, \boldsymbol{s}) \Psi_k(\boldsymbol{\xi}, 316\boldsymbol{s}) \rangle_{\boldsymbol{\xi}} ~-~ \mu^2_R(\boldsymbol{s}) 317\label{eq:sigR_comb_pce} 318\end{eqnarray} 319The remaining polynomials may then be differentiated with respect to 320$\boldsymbol{s}$. % as in Eqs.~\ref{eq:dR_dxi_pce}-\ref{eq:deriv_prod_pce}. 321In this approach, the combined PCE is valid for the full design 322variable range ($\boldsymbol{s}_L \le \boldsymbol{s} \le \boldsymbol{s}_U$) 323and does not need to be updated for each change in nonprobabilistic variables, 324although adaptive localization techniques (i.e., trust region model 325management approaches) can be employed when improved local accuracy of 326the sensitivities is required. 327% Q: how is TR ratio formed if exact soln can't be evaluated? 328% A: if objective is accuracy over a design range, then truth is PCE/SC 329% at a single design point! -->> Can use first-order corrections based 330% on the 2 different SSA approaches! This is a multifidelity SBO using 331% HF = probabilistic expansion, LF = Combined expansion. Should get data reuse. 332 333Similarly for stochastic collocation, 334\begin{equation} 335R(\boldsymbol{\xi}, \boldsymbol{s}) \cong \sum_{j=1}^{N_p} r_j 336\boldsymbol{L}_j(\boldsymbol{\xi}, \boldsymbol{s}) \label{eq:R_r_L_xi_s} 337\end{equation} 338leads to 339\begin{eqnarray} 340\mu_R(\boldsymbol{s}) &=& \sum_{j=1}^{N_p} r_j \langle 341\boldsymbol{L}_j(\boldsymbol{\xi}, \boldsymbol{s}) \rangle_{\boldsymbol{\xi}} 342\label{eq:muR_both_sc} \\ 343\sigma^2_R(\boldsymbol{s}) &=& \sum_{j=1}^{N_p} \sum_{k=1}^{N_p} r_j r_k 344\langle \boldsymbol{L}_j(\boldsymbol{\xi}, \boldsymbol{s}) 345\boldsymbol{L}_k(\boldsymbol{\xi}, \boldsymbol{s}) \rangle_{\boldsymbol{\xi}} 346~-~ \mu^2_R(\boldsymbol{s}) \label{eq:sigR_both_sc} 347\end{eqnarray} 348where the remaining polynomials not eliminated by the expectation over 349$\boldsymbol{\xi}$ are again differentiated with respect to $\boldsymbol{s}$. 350 351\subsubsection{Inputs and outputs} \label{ouu:sebdo:ssa:io} 352 353There are two types of nonprobabilistic variables for which 354sensitivities must be calculated: ``augmented,'' where the 355nonprobabilistic variables are separate from and augment the 356probabilistic variables, and ``inserted,'' where the nonprobabilistic 357variables define distribution parameters for the probabilistic 358variables. %While one could artificially augment the dimensionality of 359%a combined variable expansion approach with inserted nonprobabilistic 360%variables, this is not currently explored in this work. Thus, any 361Any inserted nonprobabilistic variable sensitivities must be handled 362using Eqs.~\ref{eq:dmuR_ds_xi_pce}-\ref{eq:dsigR_ds_xi_pce} and 363Eqs.~\ref{eq:dmuR_ds_xi_sc}-\ref{eq:dsigR_ds_xi_sc} where 364$\frac{dR}{ds}$ is calculated as $\frac{dR}{dx} \frac{dx}{ds}$ and 365$\frac{dx}{ds}$ is the Jacobian of the variable transformation 366${\bf x} = T^{-1}(\boldsymbol{\xi})$ with respect to the inserted 367nonprobabilistic variables. In addition, parameterized polynomials 368(generalized Gauss-Laguerre, Jacobi, and numerically-generated 369polynomials) may introduce a $\frac{d\Psi}{ds}$ or 370$\frac{d\boldsymbol{L}}{ds}$ dependence for inserted $s$ that will 371introduce additional terms in the sensitivity expressions. 372% TO DO: discuss independence of additional nonprobabilistic dimensions: 373% > augmented are OK. 374% > inserted rely on the fact that expansion variables \xi are _standard_ 375% random variables. 376% Special case: parameterized orthogonal polynomials (gen Laguerre, Jacobi) 377% can be differentiated w.r.t. their {alpha,beta} distribution parameters. 378% However, the PCE coefficients are likely also fns of {alpha,beta}. Therefore, 379% the approach above is correct conceptually but is missing additional terms 380% resulting from the polynomial dependence. 381% NEED TO VERIFY PCE EXPANSION DERIVATIVES FOR PARAMETERIZED POLYNOMIALS! 382 383While moment sensitivities directly enable robust design optimization 384and interval estimation formulations which seek to control or bound 385response variance, control or bounding of reliability requires 386sensitivities of tail statistics. In this work, the sensitivity of 387simple moment-based approximations to cumulative distribution function 388(CDF) and complementary cumulative distribution function (CCDF) 389mappings (Eqs.~\ref{eq:mv_ria}--\ref{eq:mv_pma}) are employed for this 390purpose, such that it is straightforward to form approximate design 391sensitivities of reliability index $\beta$ (forward reliability 392mapping $\bar{z} \rightarrow \beta$) or response level $z$ (inverse 393reliability mapping $\bar{\beta} \rightarrow z$) from the moment 394design sensitivities and the specified levels $\bar{\beta}$ or 395$\bar{z}$. 396%From here, approximate design sensitivities of probability levels may 397%also be formed given a probability expression (such as $\Phi(-\beta)$) 398%for the reliability index. The current alternative of numerical 399%design sensitivities of sampled probability levels would employ fewer 400%simplifying approximations, but would also be much more expensive to 401%compute accurately and is avoided for now. Future capabilities for 402%analytic probability sensitivities could be based on Pearson/Johnson 403%model for analytic response PDFs or 404%sampling sensitivity approaches. % TO DO: cite 405% 406%Extending beyond these simple approaches to support probability and 407%generalized reliability metrics is a subject of current work~\cite{mao2010}. 408 409 410\subsection{Optimization Formulations} \label{ouu:sebdo:form} 411 412Given the capability to compute analytic statistics of the response 413along with design sensitivities of these statistics, Dakota supports 414bi-level, sequential, and multifidelity approaches for optimization 415under uncertainty (OUU). %for reliability-based design and robust design. 416The latter two approaches apply surrogate modeling approaches (data 417fits and multifidelity modeling) to the uncertainty analysis and then 418apply trust region model management to the optimization process. 419 420\subsubsection{Bi-level SEBDO} \label{ouu:sebdo:form:bilev} 421 422The simplest and most direct approach is to employ these analytic 423statistics and their design derivatives from 424Section~\ref{ouu:sebdo:ssa} directly within an optimization loop. 425This approach is known as bi-level OUU, since there is an inner level 426uncertainty analysis nested within an outer level optimization. 427 428Consider the common reliability-based design example of a deterministic 429objective function with a reliability constraint: 430\begin{eqnarray} 431 {\rm minimize } & f \nonumber \\ 432 {\rm subject \ to } & \beta \ge \bar{\beta} \label{eq:rbdo} 433\end{eqnarray} 434where $\beta$ is computed relative to a prescribed threshold response 435value $\bar{z}$ (e.g., a failure threshold) and is constrained by a 436prescribed reliability level $\bar{\beta}$ (minimum allowable 437reliability in the design), and is either a CDF or CCDF index 438depending on the definition of the failure domain (i.e., defined from 439whether the associated failure probability is cumulative, $p(g \le 440\bar{z})$, or complementary cumulative, $p(g > \bar{z})$). 441 442Another common example is robust design in which the 443constraint enforcing a reliability lower-bound has been replaced with 444a constraint enforcing a variance upper-bound $\bar{\sigma}^2$ (maximum 445allowable variance in the design): 446\begin{eqnarray} 447 {\rm minimize } & f \nonumber \\ 448 {\rm subject \ to } & \sigma^2 \le \bar{\sigma}^2 \label{eq:rdo} 449\end{eqnarray} 450 451Solving these problems using a bi-level approach involves computing 452$\beta$ and $\frac{d\beta}{d\boldsymbol{s}}$ for 453Eq.~\ref{eq:rbdo} or $\sigma^2$ and $\frac{d\sigma^2}{d\boldsymbol{s}}$ 454for Eq.~\ref{eq:rdo} for each set of design variables $\boldsymbol{s}$ 455passed from the optimizer. This approach is supported for both 456probabilistic and combined expansions using PCE and SC. 457 458\subsubsection{Sequential/Surrogate-Based SEBDO} \label{ouu:sebdo:form:surr} 459 460An alternative OUU approach is the sequential approach, in which 461additional efficiency is sought through breaking the nested 462relationship of the UQ and optimization loops. The general concept is 463to iterate between optimization and uncertainty quantification, 464updating the optimization goals based on the most recent uncertainty 465assessment results. This approach is common with the reliability 466methods community, for which the updating strategy may be based on 467safety factors~\cite{Wu01} or other approximations~\cite{Du04}. 468 469A particularly effective approach for updating the optimization goals 470is to use data fit surrogate models, and in particular, local Taylor 471series models allow direct insertion of stochastic sensitivity 472analysis capabilities. In Ref.~\cite{Eld05}, first-order Taylor 473series approximations were explored, and in Ref.~\cite{Eld06a}, 474second-order Taylor series approximations are investigated. In both 475cases, a trust-region model management framework~\cite{Eld06b} is 476used to adaptively manage the extent of the approximations and ensure 477convergence of the OUU process. Surrogate models are used for both 478the objective and the constraint functions, although the use of 479surrogates is only required for the functions containing statistical 480results; deterministic functions may remain explicit is desired. 481 482In particular, trust-region surrogate-based optimization for 483reliability-based design employs surrogate models of $f$ and $\beta$ 484within a trust region $\Delta^k$ centered at ${\bf s}_c$: 485\begin{eqnarray} 486 {\rm minimize } & f({\bf s}_c) + \nabla_s f({\bf s}_c)^T 487({\bf s} - {\bf s}_c) \nonumber \\ 488 {\rm subject \ to } & \beta({\bf s}_c) + \nabla_s \beta({\bf s}_c)^T 489({\bf s} - {\bf s}_c) \ge \bar{\beta} \\ 490& {\parallel {\bf s} - {\bf s}_c \parallel}_\infty \le \Delta^k \nonumber 491\label{eq:rbdo_surr} 492\end{eqnarray} 493and trust-region surrogate-based optimization for robust design 494employs surrogate models of $f$ and $\sigma^2$ within a trust region 495$\Delta^k$ centered at ${\bf s}_c$: 496\begin{eqnarray} 497 {\rm minimize } & f({\bf s}_c) + \nabla_s f({\bf s}_c)^T 498({\bf s} - {\bf s}_c) \nonumber \\ 499 {\rm subject \ to } & \sigma^2({\bf s}_c) + \nabla_s \sigma^2({\bf s}_c)^T 500({\bf s} - {\bf s}_c) \le \bar{\sigma}^2 \\ 501& {\parallel {\bf s} - {\bf s}_c \parallel}_\infty \le \Delta^k \nonumber 502\label{eq:rdo_surr} 503\end{eqnarray} 504Second-order local surrogates may also be employed, where the Hessians 505are typically approximated from an accumulation of curvature 506information using quasi-Newton updates~\cite{Noc99} such as 507Broyden-Fletcher-Goldfarb-Shanno (BFGS, Eq.~\ref{eq:bfgs}) or 508symmetric rank one (SR1, Eq.~\ref{eq:sr1}). The sequential approach 509is available for probabilistic expansions using PCE and SC. 510 511\subsubsection{Multifidelity SEBDO} \label{ouu:sebdo:form:mf} 512 513The multifidelity OUU approach is another trust-region surrogate-based 514approach. Instead of the surrogate UQ model being a simple data fit 515(in particular, first-/second-order Taylor series model) of the truth 516UQ model results, distinct UQ models of differing fidelity are now 517employed. This differing UQ fidelity could stem from the fidelity of 518the underlying simulation model, the fidelity of the UQ algorithm, or 519both. In this section, we focus on the fidelity of the UQ algorithm. 520For reliability methods, this could entail varying fidelity in 521approximating assumptions (e.g., Mean Value for low fidelity, SORM for 522high fidelity), and for stochastic expansion methods, it could involve 523differences in selected levels of $p$ and $h$ refinement. 524 525%Here we will explore multifidelity stochastic models and employ 526%first-order additive corrections, where the meaning of multiple 527%fidelities is expanded to imply the quality of multiple UQ analyses, 528%not necessarily the fidelity of the underlying simulation model. For 529%example, taking an example from the reliability method family, one 530%might employ the simple Mean Value method as a ``low fidelity'' UQ 531%model and take SORM as a ``high fidelity'' UQ model. In this case, 532%the models do not differ in their ability to span a range of design 533%parameters; rather, they differ in their sets of approximating 534%assumptions about the characteristics of the response function. 535 536Here, we define UQ fidelity as point-wise accuracy in the design space 537and take the high fidelity truth model to be the probabilistic 538expansion PCE/SC model, with validity only at a single design point. 539The low fidelity model, whose validity over the design space will be 540adaptively controlled, will be either the combined expansion PCE/SC 541model, with validity over a range of design parameters, or the MVFOSM 542reliability method, with validity only at a single design point. The 543combined expansion low fidelity approach will span the current trust 544region of the design space and will be reconstructed for each new 545trust region. Trust region adaptation will ensure that the combined 546expansion approach remains sufficiently accurate for design purposes. 547By taking advantage of the design space spanning, one can eliminate 548the cost of multiple low fidelity UQ analyses within the trust region, 549with fallback to the greater accuracy and higher expense of the 550probabilistic expansion approach when needed. The MVFOSM low fidelity 551approximation must be reformed for each change in design variables, 552but it only requires a single evaluation of a response function and 553its derivative to approximate the response mean and variance from the 554input mean and covariance 555(Eqs.~\ref{eq:mv_mean1}--\ref{eq:mv_std_dev}) from which 556forward/inverse CDF/CCDF reliability mappings can be generated using 557Eqs.~\ref{eq:mv_ria}--\ref{eq:mv_pma}. This is the least expensive UQ 558option, but its limited accuracy\footnote{MVFOSM is exact for linear 559 functions with Gaussian inputs, but quickly degrades for nonlinear 560 and/or non-Gaussian.} may dictate the use of small trust regions, 561resulting in greater iterations to convergence. The expense of 562optimizing a combined expansion, on the other hand, is not 563significantly less than that of optimizing the high fidelity UQ model, 564but its representation of global trends should allow the use of larger 565trust regions, resulting in reduced iterations to convergence. 566%While conceptually different, in the end, this approach is 567%similar to the use of a global data fit surrogate-based optimization 568%at the top level in combination with the probabilistic expansion PCE/SC 569%at the lower level, with the distinction that the multifidelity approach 570%embeds the design space spanning within a modified PCE/SC process 571%whereas the data fit approach performs the design space spanning 572%outside of the UQ (using data from a single unmodified PCE/SC process, 573%which may now remain zeroth-order). 574The design derivatives of each of the PCE/SC expansion models provide 575the necessary data to correct the low fidelity model to first-order 576consistency with the high fidelity model at the center of each trust 577region, ensuring convergence of the multifidelity optimization process 578to the high fidelity optimum. Design derivatives of the MVFOSM 579statistics are currently evaluated numerically using forward finite 580differences. 581 582Multifidelity optimization for reliability-based design can be 583formulated as: 584\begin{eqnarray} 585 {\rm minimize } & f({\bf s}) \nonumber \\ 586 {\rm subject \ to } & \hat{\beta_{hi}}({\bf s}) \ge \bar{\beta} \\ 587& {\parallel {\bf s} - {\bf s}_c \parallel}_\infty \le \Delta^k \nonumber 588\label{eq:rbdo_mf} 589\end{eqnarray} 590and multifidelity optimization for robust design can be formulated as: 591\begin{eqnarray} 592 {\rm minimize } & f({\bf s}) \nonumber \\ 593 {\rm subject \ to } & \hat{\sigma_{hi}}^2({\bf s}) \le \bar{\sigma}^2 \\ 594& {\parallel {\bf s} - {\bf s}_c \parallel}_\infty \le \Delta^k \nonumber 595\label{eq:rdo_mf} 596\end{eqnarray} 597where the deterministic objective function is not approximated and 598$\hat{\beta_{hi}}$ and $\hat{\sigma_{hi}}^2$ are the approximated 599high-fidelity UQ results resulting from correction of the low-fidelity 600UQ results. In the case of an additive correction function: 601\begin{eqnarray} 602\hat{\beta_{hi}}({\bf s}) &=& \beta_{lo}({\bf s}) + 603\alpha_{\beta}({\bf s}) \label{eq:corr_lf_beta} \\ 604\hat{\sigma_{hi}}^2({\bf s}) &=& \sigma_{lo}^2({\bf s}) + 605\alpha_{\sigma^2}({\bf s}) \label{eq:corr_lf_sigma} 606\end{eqnarray} 607where correction functions $\alpha({\bf s})$ enforcing first-order 608%and quasi-second-order 609consistency~\cite{Eld04} are typically employed. Quasi-second-order 610correction functions~\cite{Eld04} can also be employed, but care 611must be taken due to the different rates of curvature accumulation 612between the low and high fidelity models. In particular, since the 613low fidelity model is evaluated more frequently than the high fidelity 614model, it accumulates curvature information more quickly, such that 615enforcing quasi-second-order consistency with the high fidelity model 616can be detrimental in the initial iterations of the 617algorithm\footnote{Analytic and numerical Hessians, when 618available, are instantaneous with no accumulation rate concerns.}. 619Instead, this consistency should only be enforced when sufficient high 620fidelity curvature information has been accumulated (e.g., after $n$ 621rank one updates). 622 623 624 625\section{Sampling-based OUU}\label{ouu:sampling} 626 627Gradient-based OUU can also be performed using random sampling methods. 628In this case, the sample-average approximation to the design derivative 629of the mean and standard deviation are: 630\begin{eqnarray} 631 \frac{d\mu}{ds} &=& \frac{1}{N} \sum_{i=1}^N \frac{dQ}{ds} \\ 632 \frac{d\sigma}{ds} &=& \left[ \sum_{i=1}^N (Q \frac{dQ}{ds}) 633 - N \mu \frac{d\mu}{ds} \right] / (\sigma (N-1)) 634\end{eqnarray} 635This enables design sensitivities for mean, standard deviation or 636variance (based on {\tt final\_moments} type), and forward/inverse 637reliability index mappings 638($\bar{z} \rightarrow \beta$, $\bar{\beta} \rightarrow z$). 639 640% TO DO: Multilevel MC ... 641