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ABSTRACT Electromagnetic (EM) fields have been used in technologies such as communication, imaging, and energy transfer. In recent years, there has been growing interest in exploiting EM fields for the actuation of functional materials, enabling applications in soft robotics, biomedical devices, active metamaterials, and shape‐morphing systems. These materials are often composites that incorporate EM‐responsive components, granting them a remarkable versatility in responsiveness. Specifically, EM fields can induce actuation through static magnetic force and torque, Lorentz forces, or thermal effects via eddy currents and magnetic hysteresis losses. In addition, EM fields can be harnessed for sensing, wireless communication, and power transfer, extending their role far beyond actuation. The coexistence of such diverse mechanisms makes EM one of the most powerful and integrative external stimuli for multifunctional materials. This review provides the first holistic overview of EM‐active material systems. We systematically organize recent progress in EM‐based actuation, sensing, communication, and wireless power transfer, highlighting the fundamental principles, experimental demonstrations, and emerging design strategies. Approaches that integrate multiple EM‐driven functionalities and the role of optimization and machine learning in advancing design and control are discussed. By consolidating these advances, this review establishes a roadmap for the development of next‐generation EM‐enabled intelligent materials and devices. 

In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different conditions. Recent advancements in neural operators, such as deep operator network (DeepONet), which learn mappings between infinite-dimensional function spaces, promise efficient computation of PDE solutions for a new condition in a single forward pass. However, classical DeepONet entails quadratic complexity concerning input dimensions during evaluation. Given the progress in quantum algorithms and hardware, here we propose to utilize quantum computing to accelerate DeepONet evaluations, yielding complexity that is linear in input dimensions. Our proposed quantum DeepONet integrates unary encoding and orthogonal quantum layers. We benchmark our quantum DeepONet using a variety of PDEs, including the antiderivative operator, advection equation, and Burgers' equation. We demonstrate the method's efficacy in both ideal and noisy conditions. Furthermore, we show that our quantum DeepONet can also be informed by physics, minimizing its reliance on extensive data collection. Quantum DeepONet will be particularly advantageous in applications in outer loop problems which require exploring parameter space and solving the corresponding PDEs, such as uncertainty quantification and optimal experimental design. 

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full‐field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data‐driven models for learning full‐field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full‐field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics‐informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics‐informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo‐Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.