This research presents an AI approach to designing linkage systems, which are crucial in engineering for transforming rotational movement into precise periodic paths.
This study examines how diverse initialization, such as Latin Hypercube Sampling (LHS) or Space-Filling Designs, impacts Bayesian Optimization (BO), one of the most widely used gradient-free optimizers.
This featured article introduces a self-learning design agent, an innovative approach that doesn’t rely on human data or problem-specific knowledge. Instead, it uses deep learning and tree search to find high-performing and generalizable engineering design strategies.
In our modern world, complex systems such as power grids, transportation networks, and communication systems can be disrupted by both internal uncertainties and external factors like natural disasters. To make these systems more resilient, researchers have been working on design strategies that improve both reliability and recovery capabilities.
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.
From smartphones to electric cars, lithium-ion batteries allow us power our favorite devices. When used in our homes, they allow us to store cheap electricity for later use.
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