Generating sequential space-filling designs using genetic algorithms and Monte Carlo methods

In this paper, the authors compare a Monte Carlo method and an optimization-based approach using genetic algorithms for sequentially generating space-filling experimental designs. It is shown that Monte Carlo methods perform better than genetic algorithms for this specific problem

Robust Counterparts of Inequalities Containing Sums of Maxima of Linear Functions

This paper adresses the robust counterparts of optimization problems containing sums of maxima of linear functions and proposes several reformulations. These problems include many practical problems, e.g. problems with sums of absolute values, and arise when taking the robust counterpart of a linear inequality that is affine in the decision variables, affine in a parameter with box uncertainty, and affine in a parameter with general uncertainty. In the literature, often the reformulation that is exact when there is no uncertainty is used. However, in robust optimization this reformulation gives an inferior solution and provides a pessimistic view. We observe that in many papers this conservatism is not mentioned. Some papers have recognized this problem, but existing solutions are either too conservative or their performance for different uncertainty regions is not known, a comparison between them is not available, and they are restricted to specific problems. We provide techniques for general problems and compare them with numerical examples in inventory management, regression and brachytherapy. Based on these examples, we give tractable recommendations for reducing the conservatism.robust optimization;sum of maxima of linear functions;biaffine uncertainty;robust conic quadratic constraints

Surrogate modeling for fast experimental assessment of specific absorption rate

Experimental dosimetry of electromagnetic fields (EMFs) in biological tissue is important for validating numerical techniques, designing electromagnetic exposure systems, and compliance testing of wireless devices. Compliance standards specify a two-step procedure to determine the peak spatial-averaged SAR in 1 g or 10 g of tissue in which the measurement locations lie on a rectilinear grid (selected up-front). In this chapter, we show the potential of surrogate modeling techniques to significantly reduce the duration of experimental dosimetry of EMF by using a sequential design. A sequential design or adaptive sampling differs from a traditional design of experiments as data and models from previous iterations are used to optimally select new samples resulting in a more efficient distribution of samples as compared with the traditional design of experiments. Based on a data set of about 100 dosimetric measurements, we show that the adaptive sampling of surrogate modeling is suitable to speed up the determination of the peak SAR location in an area scan by up to 43 and 64% compared with the standardized area scan on a rectilinear grid (IEC 622090, IEEE Std 1528:2013) for the LOLA-Voronoi-error and the LOLA-max surrogate model, respectively

Universal Prediction Distribution for Surrogate Models

International audienceThe use of surrogate models instead of computationally expensive simulation codes is very convenient in engineering. Roughly speaking, there are two kinds of surrogate models: the deterministic and the probabilistic ones. These last are generally based on Gaussian assumptions. The main advantage of probabilistic approach is that it provides a measure of uncertainty associated with the surrogate model in the whole space. This uncertainty is an efficient tool to construct strategies for various problems such as prediction enhancement, optimization or inversion.In this paper, we propose a universal method to define a measure of uncertainty suitable for any surrogate model either deterministic or probabilistic. It relies on Cross-Validation (CV) sub-models predictions. This empirical distribution may be computed in much more general frames than the Gaussian one. So that it is called the Universal Prediction distribution (UP distribution).It allows the definition of many sampling criteria. We give and study adaptive sampling techniques for global refinement and an extension of the so-called Efficient Global Optimization (EGO) algorithm. We also discuss the use of the UP distribution for inversion problems. The performances of these new algorithms are studied both on toys models and on an engineering design problem

SU8 etch mask for patterning PDMS and its application to flexible fluidic microactuators.

Over the past few decades, polydimethylsiloxane (PDMS) has become the material of choice for a variety of microsystem applications, including microfluidics, imprint lithography, and soft microrobotics. For most of these applications, PDMS is processed by replication molding; however, new applications would greatly benefit from the ability to pattern PDMS films using lithography and etching. Metal hardmasks, in conjunction with reactive ion etching (RIE), have been reported as a method for patterning PDMS; however, this approach suffers from a high surface roughness because of metal redeposition and limited etch thickness due to poor etch selectivity. We found that a combination of LOR and SU8 photoresists enables the patterning of thick PDMS layers by RIE without redeposition problems. We demonstrate the ability to etch 1.5-μm pillars in PDMS with a selectivity of 3.4. Furthermore, we use this process to lithographically process flexible fluidic microactuators without any manual transfer or cutting step. The actuator achieves a bidirectional rotation of 50° at a pressure of 200 kPa. This process provides a unique opportunity to scale down these actuators as well as other PDMS-based devices.BG is a Doctoral Fellow of the Research Foundation—Flanders (F.W.O.), Belgium. MDV acknowledges support from the ERC starting grant HIENA (no. 337739)

Hardware Sequencing of Inflatable Nonlinear Actuators for Autonomous Soft Robots

Soft robots are an interesting alternative for classic rigid robots in appli-cations requiring interaction with organisms or delicate objects. Elastic inflatable actuators are one of the preferred actuation mechanisms for soft robots since they are intrinsically safe and soft. However, these pneumatic actuators each require a dedicated pressure supply and valve to drive and control their actuation sequence. Because of the relatively large size of pres-sure supplies and valves compared to electrical leads and electronic control-lers, tethering pneumatic soft robots with multiple degrees of freedom is bulky and unpractical. Here, a new approach is described to embed hardware intelligence in soft robots where multiple actuators are attached to the same pressure supply, and their actuation sequence is programmed by the inter-action between nonlinear actuators and passive flow restrictions. How to model this hardware sequencing is discussed, and it is demonstrated on an 8-degree-of-freedom walking robot where each limb comprises two actua-tors with a sequence embedded in their hardware. The robot is able to carry pay loads of 800 g in addition to its own weight and is able to walk at travel speeds of 3 body lengths per minute, without the need for complex on-board valves or bulky tethers.ERC starting gran