Special Issue: Machine Learning for Engineering Design

摘要

Modern machine learning (ML) techniques are transforming many disciplines ranging from transportation to healthcare by uncovering patterns in data, developing autonomous systems that mimic human abilities, and supporting human decision-making. Modern ML techniques, such as deep neural networks, are fueling the rapid developments in artificial intelligence. Engineering design researchers have increasingly used and developed ML techniques to support a wide range of activities from preference modeling to uncertainty quantification in high-dimensional design optimization problems. This special issue brings together fundamental scientific contributions across these areas.The special issue consists of 24 papers spread over two issues of the Journal of Mechanical Design. The papers use various ML techniques, including artificial neural networks, Gaussian processes, reinforcement learning, clustering techniques, and natural language processing. Based on their research objective, the papers can be broadly classified into four groups: (i) ML to support surrogate modeling, design exploration, and optimization, (ii) ML for design synthesis, (iii) ML for extracting human preferences and design strategies, and (iv) comparative studies of ML techniques and research platforms to help design researchers. The papers are summarized in Secs. 1–4. An analysis of the themes covered in the special issue and the potential opportunities for future research in ML for Engineering Design are presented in Sec. 5.In the paper titled Multifidelity Physics-Constrained Neural Network and Its Application in Materials Modeling, Liu and Yang address how to incorporate multifidelity, physics-based constraints into neural network predictions. The paper contributes two key insights. First, the paper extends existing Physics-Constraints Neural Network architectures by imposing a multifidelity constraint scheme wherein an auxiliary network minimizes discrepancies between low and high fidelity models—essentially learning how to correct the low-fidelity one. Second, it proposes an adaptive weighting scheme to control the convergence of individual losses among the different fidelities. They demonstrate the impact of these improvements on several fundamental multiscale material modeling challenges including two-dimensional heat transfer, phase transition, and dendritic growth problems. On these problems, the proposed multifidelity, physics-based constraints decrease the prediction error up to order of magnitude compared with networks without such constraints. This achieves comparable accuracy to that of direct numerical solutions of the underlying equations.Sarkar et al. present a multifidelity modeling and information-theoretic sequential sampling strategy for optimization in their paper titled Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration. The approach is based on modeling of the varied fidelity information sources via Gaussian processes, augmented with efficient active learning strategies that involve sequential selection of optimal points in a multiscale architecture. The strategy is demonstrated using the design optimization of a compressor rotor and calibration of a microstructure prediction model.In the paper titled A Case Study of Deep Reinforcement Learning for Engineering Design: Application to Microfluidic Devices for Flow Sculpting, Lee et al. address how to design micro-fluidic flow sculpting devices by overcoming some of the key weaknesses of evolutionary optimization-based methods, namely, poor sample efficiency and slow optimization convergence. The paper adapts deep reinforcement learning (DRL) techniques to the flow sculpting task and also studies the effectiveness of transfer learning on accelerating the design of target flow shapes. The paper demonstrates that DRL is able to match 90% of the target flow shapes using significantly fewer sculpting pillars than comparable GA models as well as prov