Often in the design process of an engineer, the design specifications of the system are not completely known initially. However, usually there are some physical constraints which are already known, corresponding to a region of interest in the design space that is called feasible. These constraints often have no analytical form but need to be characterised based on expensive simulations or measurements. Therefore, it is important that the feasible region can be modeled sufficiently accurate using only a limited amount of samples. This can be solved by using active learning techniques that minimize the amount of samples w.r.t. what we try to model. Most active learning strategies focus on classification models or regression models with classification accuracy and regression accuracy in mind respectively. In this work, regression models of the constraints are used, but only the (in)feasibility is of interest. To tackle this problem, an information-theoretic sampling strategy is constructed to discover these regions. The proposed method is then tested on two synthetic examples and one engineering example and proves to outperform the current stateof-the-art.
Dynamic stability is a key performance metric of motor vehicles and has a direct impact on passenger experience and customer satisfaction. The desired vehicle dynamics behavior can be achieved by optimizing the design of vehicle suspensions. Two challenges are associated with this design optimization task. The first one arises from the large number (e.g., 40 or 50) of design variables in modern suspension systems. Such multitude of variables not only makes it expensive to build a training dataset for metamodeling purposes, but also renders accurate surrogate modeling extremely difficult. The second challenge is a lack of guideline for choosing a proper multidisciplinary design optimization (MDO) method for a single MDO problem such as one for vehicle suspension design. In this paper, an enhanced Gaussian process (GP) metamodeling technique is developed and several versions of the collaborative optimization (CO) method are compared via a vehicle suspension design problem. In our enhanced GP modeling method, the model parameters are efficiently estimated using the smoothing effect of the so-called nugget parameter to reduce the search space. In addition, various versions of the CO method are studied where the enhanced collaborative optimization (ECO) method is found to perform the best. A simplified ECO formulation is also investigated to provide insights for future engineering applications.
Enabled by advancements in multi-material additive manufacturing, lightweight lattice structures consisting of networks of periodic unit cells have gained popularity due to their extraordinary performance and wide array of functions. This work proposes a density-based robust topology optimization method for meso-or macroscale multi-material lattice structures under any combination of material and load uncertainties. The method utilizes a new generalized material interpolation scheme for an arbitrary number of materials, and employs univariate dimension reduction and Gauss-type quadrature to quantify and propagate uncertainty. By formulating the objective function as a weighted sum of the mean and standard deviation of compliance, the tradeoff between optimality and robustness can be studied and controlled. Examples of a cantilever beam lattice structure under various material and load uncertainty cases exhibit the efficiency and flexibility of the approach. The accuracy of univariate dimension reduction is validated by comparing the results to the Monte Carlo approach.
We examine who is the repository of soft information within bank organizations. Inconsistent with the conventional view of loan officers as the sole repository, we find that branch managers have the most soft information. We also find the repository at a higher hierarchical level at smaller banks. Furthermore, our evidence suggests that branch managers themselves actively collect soft information, especially at smaller banks. These findings suggest the need for a more nuanced view beyond the conventional emphasis on loan officers, and call for studies on the equilibrium design of the collection, processing, and use of soft information within bank organizations.JEL codes: D2, D8, L2, G21
We compare aggregate wage dynamics in Japan and the US from 1970 to 2013 from the perspective of the New Keynesian wage Phillips curve (NKWPC), derived by [Galí, Jordi. 2011a. “The Return of the Wage Phillips Curve.” Journal of the European Economic Association 9 (3): 436–461.]. We consider time variations in NKWPC’s parameters and make comparisons with micro-based evidence. Our main findings are three-fold. First, although Japan’s NKWPC has flattened over time, the slope of the NKWPC is much steeper in Japan than in the US. This suggests that nominal wage changes are less frequent in the US than in Japan. Second, inflation indexation, more prevalent in Japan in earlier periods, has recently become more important in the US although its role has declined over time in both countries. Third, our macro-level empirical results on the NKWPC are generally in line with micro-based evidence in each country, which suggests that the NKWPC provides a reasonable platform for modeling aggregate wage dynamics.
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