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Russia is one of the few countries where surrogacy is both legal and regulated. Still, volatile legislation and the lack of public acceptance of the practice make surrogacy an experience that is hard to navigate. This chapter presents an exploration of the meanings Russian surrogates attach to their work, remuneration for it, and their relationships with intended parents. Drawing on 23 semi-structured interviews with surrogates, we find that while Russian surrogates frame surrogacy as a job and engage in calculations of a fair price for their services, they provide unrequited care for intended parents and their children and embed surrogacy in the context of their motherhood as a way to provide and care for their own children. In this, Russian surrogates occupy the typical position of a post-Soviet ‘mother-worker’.

This chapter examines the emergence of India as a site for surrogacy, which has led intended parents from all over the world to contract with Indian gestational surrogates to carry “their” babies for them. Through participant observation in a surrogacy workshop, interviews with American intended parents, and interviews with Indian surrogates, I show how ideologies of normative, nuclear families built around genetically similar children, drives American consumers' desires to seek fertility intervention, and, finally, surrogacy. In India, gender ideologies shape the contours of an inexpensive, compliant labor force of surrogate mothers.

The conventional design method relies on a priori knowledge, which limits the rapid and efficient development of electronic packaging structures. The purpose of this study is to propose a hybrid method of data-driven inverse design, which couples adaptive surrogate model technology with optimization algorithm to to enable an efficient and accurate inverse design of electronic packaging structures.

The multisurrogate accumulative local error-based ensemble forward prediction model is proposed to predict the performance properties of the packaging structure. As the forward prediction model is adaptive, it can identify respond to sensitive regions of design space and sample more design points in those regions, getting the trade-off between accuracy and computation resources. In addition, the forward prediction model uses the average ensemble method to mitigate the accuracy degradation caused by poor individual surrogate performance. The Particle Swarm Optimization algorithm is then coupled with the forward prediction model for the inverse design of the electronic packaging structure.

Benchmark testing demonstrated the superior approximate performance of the proposed ensemble model. Two engineering cases have shown that using the proposed method for inverse design has significant computational savings while ensuring design accuracy. In addition, the proposed method is capable of outputting multiple structure parameters according to the expected performance and can design the packaging structure based on its extreme performance.

Because of its data-driven nature, the inverse design method proposed also has potential applications in other scientific fields related to optimization and inverse design.

Surrogate models are extensively used to substitute real models which are expensive to evaluate in the time-dependent reliability analysis. Normally, different surrogate models have different scopes of application. However, information is often insufficient for analysts to select the most appropriate surrogate model for a specific application. Thus, the result precited by individual surrogate model tends to be suboptimal or even inaccurate. Ensemble model can effectively deal with the above concern. This work aims to study the application of ensemble model for reliability analysis of time-independent problems.

In this work, a method of reliability analysis for time-dependent problems based on ensemble learning of surrogate models is developed. The ensemble of surrogate models includes Kriging, radial basis function, and support vector machine. The prediction is approximated by the weighted average model. The ensemble learning of surrogate models is updated by finding and adding the sample points with large prediction errors throughout the entire procedure.

The effectiveness of the proposed method is verified by several examples. The results show that the ensemble of surrogate models can effectively propagate the uncertainty of time-varying problems, and evaluate the reliability with high prediction accuracy and computational efficiency.

This work proposes an adaptive learning framework for the uncertainty propagation of time-dependent problems based on the ensemble of surrogate models. Compared with individual surrogate models, the ensemble model not only saves the effort of selecting an appropriate surrogate model especially when the knowledge of unknown problem is lacking, but also improves the prediction accuracy and computational efficiency.

This study aims to predict dynamic responses of aileron and spoiler control surfaces in subsonic flight via the use of surrogate models. The prepared reduced order models prove useful when quick estimations for a large number of variations are required.

The linear frequency domain (LFD) method was used for the simulation study. Each surrogate contained a database of 100 control surface dynamic responses over a spectrum of 200 harmonics computed with LFD. To interpolate new results, the DLR surrogate modelling toolbox, SMARTy, was used. The database’s samples were prepared in a Halton sequence, making interpolation reliable. The surrogate’s parameter space was the Mach number, Reynold’s number, angle of attack, control surface deflection angle and the control surface chord length.

The LFD method proved effective for the mentioned purpose: the surrogates were accurate, up to 15% of relative error, in reproducing dynamic responses of aileron and spoiler deflections at low speed, within the limitations of flow field linearity, as well as surrogate prediction capability. The restrictions of the surrogate, and the reasoning thereof, are also presented in detail in the study. Future load alleviation studies are a potential of the findings here.

LFD is an innovative technique for load prediction and alleviation studies. This paper provides a reference for engineers wishing to use the method for the two mentioned control surfaces, or the like.

Models the role and impact of a relatively new intermediary, the surrogate buyers, on the new product adoption process. Existing diffusion models have ignored the impact of this intermediary who is becoming increasingly influential in many product categories/ purchase situations. Given the increasing product complexity and a plethora of product‐related information in the market, buyers are delegating the task of processing this information and making purchase decisions to surrogate buyers (such as wardrobe consultants, interior decorators). Examines the impact of such delegation on the adoption process. The inclusion of surrogate buyers not only makes the adoption process two‐staged, but also has other important managerial implications because of the unique characteristics of surrogate buyers. Develops a conceptual model to examine the impact of these characteristics on the adoption process and presents strategies to market new products successfully in situations where the surrogate buyers are the primary adopting units.

The purpose of this paper is to propose physically based varying fidelity surrogates to be used in structural design optimization of space trusses. The main aim is to demonstrate its efficiency in reducing the number of high fidelity (HF) runs in the optimization process.

In this work, surrogate models are built for space truss structures. This study uses functional as well as physical surrogates. In the latter, a grid analogy of the space truss is used thereby reducing drastically the analysis cost. Global and local approaches are considered. The latter will require a globalization scheme (sequential approximate optimization (SAO)) to ensure convergence.

Physically based surrogates were proposed. Classical techniques, namely Taylor series and kriging, are also implemented for comparison purposes. A parameter study in kriging is necessary to select the best kriging model to be used as surrogate. A test case was considered for optimization and several surrogates were built. The CPU time is reduced when compared with the HF solution, for all surrogate‐based optimization performed. The best result was achieved combining the proposed physical model with additive corrections in a SAO strategy in which C1 continuity was imposed at each trust region center. Some guidance for other engineering applications was given.

This is the first time that physical‐based surrogates for optimum design of space truss systems are used in the SAO framework. Physical surrogates typically exhibit better generalization properties than other surrogates forms, produce faster solutions, and do not suffer from dimensionality curse when used in approximate optimization strategies.

This paper aims to set up and assess a new method to collaboratively mature the requirements for engine development in a more efficient way during the preliminary design phase.

A collaborative process has been set up in which detailed information on the behaviour of designed engines has been integrated into the aircraft preliminary sizing process by means of surrogate modelling.

The engine surrogate model has been invoked as a black box from within the aircraft preliminary design optimisation loops. The surrogate model reduces the uncertainty of coarse-grain formulas and may result in more competitive aircraft and engine designs. The surrogate model has been integrated in a collaborative cross-organisational workflow between aircraft manufacturer, engine manufacturer and simulation service providers to prepare for its deployment in industrial preliminary design processes.

The new collaborative way of working between aircraft manufacturer, engine manufacturer and simulation service providers could contribute to remove time consuming rework cycles in early and later design stages within delivering the optimal aircraft-engine combination.

The assessed process, based on an innovative collaboration standard, provides the opportunity to introduce useful design iterations with much more enriched information than in the classical design process as performed today. Specifically, the application of an engine surrogate model is advantageous, as it allows for extensive trade-off studies on aircraft level because of the low computational effort, while the intellectual property of the engine manufacturer (the engine preliminary design process) is respected and kept in-house.

In multidisciplinary design optimization (MDO), if the relationships between design variables and some output parameters, which are important performance constraints, are complex implicit problems, plenty of time should be spent on computationally expensive simulations to identify whether the implicit constraints are satisfied with the given design variables during the optimization iteration process. The purpose of this paper is to propose an ensemble of surrogates-based analytical target cascading (ESATC) method to tackle such MDO engineering design problems with reduced computational cost and high optimization accuracy.

Different surrogate models are constructed based on the sample point sets obtained by Latin hypercube sampling (LHS) method. Then, according to the error metric of each surrogate model, the repeated ensemble of surrogates is constructed to approximate the implicit objective functions and constraints. Under the framework of analytical target cascading (ATC), the MDO problem is decomposed into several optimization subproblems and the function of analysis module of each subproblem is simulated by repeated ensemble of surrogates, working together to find the optimum solution.

The proposed method shows better modeling accuracy and robustness than other individual surrogate model-based ATC method. A numerical benchmark problem and an industrial case study of the structural design of a super heavy vertical lathe machine tool are utilized to demonstrate the accuracy and efficiency of the proposed method.

This paper integrates a repeated ensemble method with ATC strategy to construct the ESATC framework which is an effective method to solve MDO problems with implicit constraints and black-box objectives.

To date e‐Learning material has usually been accessed and delivered through a central web server. As the number of users, the amount of information, the frequency of accesses and the volume of data increase, together with the introduction of multimedia streaming applications, a decentralized content distribution architecture is necessary. In this paper we propose the adaptation of the well‐known scalable Content Distribution Networks (CDN) schema for media streaming supported e‐Learning using a novel architecture named COMODIN SCDN (COoperative Media On‐Demand on the InterNet ‐ Streaming Content Distribution Network). COMODIN SCDN utilises surrogates as edge content delivery nodes, incorporates a redirection mechanism able to route requesting clients to the closest copy of the content, encompasses distributed content delivery and management mechanisms to improve the speed, reliability, and scalability of user access to prevent flash‐crowds. Preliminary results in testbeds have shown that COMODIN SCDN increases the efficacy of information distribution through intra and inter‐campus area netwoks. This overlay network will provide learners and educators a scalable, balanced and expeditious access to e‐Learning contents.