Researchers have succeeded in developing the smallest fully autonomous robot in history. It measures less than 1 millimeter and can swim underwater for months powered only by light. Miniaturization has long been a challenge in the history of robotics . While engineers have made great strides in the miniaturization of electronics in the past few decades, builders of miniature autonomous robots have not been able to meet the goal of getting them under 1 millimeter in size. This is because small arms and legs are fragile and difficult to manufacture.

Abstract-- Multilayer piezoelectric polyvinylidene fluoride (PVDF) actuators are a promising approach to enhance performance of soft microrobotic systems. In this work, we develop and characterize multilayer PVDF actuators with parallel voltage distribution across each layer, bridging a unique design space between brittle high-force PZT stacks and compliant but lower-bandwidth soft polymer actuators. We show the effects of layer thickness and number of layers in actuator performance and their agreement with a first principles model. By varying these parameters, we demonstrate actuators capable of >3 mm of free deflection, >20 mN of blocked force, and >=500 Hz, while operating at voltages as low as 150 volts. T o illustrate their potential for robotic integration, we integrate our actuators into a planar, translating microrobot that leverages resonance to achieve locomotion with robustness to large perturbations.

In 2024, the "low-altitude economy" was written into China's Government Work Report for the first time [1], and flying robots have been rapidly popularized nationwide. From an environmental perspective, electrically powered air vehicles are attracting growing attention; key technologies include overall configuration design, integrated energy management, and high-efficiency, high power-to-weight electric propulsion [2]. For electric propulsion, mainstream systems use electric motors to drive propellers, but propeller noise is significant and hard to mitigate [3], which limits widespread use in cities--the main arena for the low-altitude economy--and is also unfavorable for silent reconnaissance. Hence, there is a pressing need for a new propulsion approach enabling quiet, fully electric flight. In the 1920s, Brown observed that an asymmetric capacitor under high voltage can generate thrust, known as the Biefeld-Brown effect. A leading explanation is ionic wind: a high electric field ionizes air, and the resulting ions accelerate and transfer momentum to neutral molecules, producing a net airflow (thrust) [4]. Xu et al. first mounted a plasma thruster on a fixed-wing UAV without other propulsion; the gliding distance with the thruster on was five times that with it off, but the maximum range was only 45m and no controller design was provided [5]. Zhang et al. realized altitude control for a micro ionic-wind-powered UA V using passive components, but the wingspan was at most 6 .3cm

We introduce PHYSICS, a comprehensive benchmark for university-level physics problem solving. It contains 1297 expert-annotated problems covering six core areas: classical mechanics, quantum mechanics, thermodynamics and statistical mechanics, electromagnetism, atomic physics, and optics. Each problem requires advanced physics knowledge and mathematical reasoning. We develop a robust automated evaluation system for precise and reliable validation. Our evaluation of leading foundation models reveals substantial limitations. Even the most advanced model, o3-mini, achieves only 59.9% accuracy, highlighting significant challenges in solving high-level scientific problems. Through comprehensive error analysis, exploration of diverse prompting strategies, and Retrieval-Augmented Generation (RAG)-based knowledge augmentation, we identify key areas for improvement, laying the foundation for future advancements.

Radio-Frequency (RF) imaging concerns the digital recreation of the surfaces of scene objects based on the scattered field at distributed receivers. To solve this difficult inverse scattering problems, data-driven methods are often employed that extract patterns from similar training examples, while offering minimal latency. In this paper, we first provide an approximate yet fast electromagnetic model, which is based on the electric field integral equations, for data generation, and subsequently propose a Deep Neural Network (DNN) architecture to learn the corresponding inverse model. A graph-attention backbone allows for the system geometry to be passed to the DNN, where residual convolutional layers extract features about the objects, while a UNet head performs the final image reconstruction. Our quantitative and qualitative evaluations on two synthetic data sets of different characteristics showcase the performance gains of thee proposed advanced architecture and its relative resilience to signal noise levels and various reception configurations.

In this study, we unveil a new AI model, termed PhyE2E, to discover physical formulas through symbolic regression. PhyE2E simplifies symbolic regression by decomposing it into sub-problems using the second-order derivatives of an oracle neural network, and employs a transformer model to translate data into symbolic formulas in an end-to-end manner. The resulting formulas are refined through Monte-Carlo Tree Search and Genetic Programming. We leverage a large language model to synthesize extensive symbolic expressions resembling real physics, and train the model to recover these formulas directly from data. A comprehensive evaluation reveals that PhyE2E outperforms existing state-of-the-art approaches, delivering superior symbolic accuracy, precision in data fitting, and consistency in physical units. We deployed PhyE2E to five applications in space physics, including the prediction of sunspot numbers, solar rotational angular velocity, emission line contribution functions, near-Earth plasma pressure, and lunar-tide plasma signals. The physical formulas generated by AI demonstrate a high degree of accuracy in fitting the experimental data from satellites and astronomical telescopes. We have successfully upgraded the formula proposed by NASA in 1993 regarding solar activity, and for the first time, provided the explanations for the long cycle of solar activity in an explicit form. We also found that the decay of near-Earth plasma pressure is proportional to r^2 to Earth, where subsequent mathematical derivations are consistent with satellite data from another independent study. Moreover, we found physical formulas that can describe the relationships between emission lines in the extreme ultraviolet spectrum of the Sun, temperatures, electron densities, and magnetic fields. The formula obtained is consistent with the properties that physicists had previously hypothesized it should possess.

A surrogate model for particle-in-cell plasma simulations based on a graph neural network is presented. The graph is constructed in such a way as to enable the representation of electromagnetic fields on a fixed spatial grid. The model is applied to simulate beams of electrons in one dimension over a wide range of temperatures, drift momenta and densities, and is shown to reproduce two-stream instabilities - a common and fundamental plasma instability. Qualitatively, the characteristic phase-space mixing of counterpropagating electron beams is observed. Quantitatively, the model's performance is evaluated in terms of the accuracy of its predictions of number density distributions, the electric field, and their Fourier decompositions, particularly the growth rate of the fastest-growing unstable mode, as well as particle position, momentum distributions, energy conservation and run time. The model achieves high accuracy with a time step longer than conventional simulation by two orders of magnitude. This work demonstrates that complex plasma dynamics can be learned and shows promise for the development of fast differentiable simulators suitable for solving forward and inverse problems in plasma physics.

We propose Electrostatic Field Matching (EFM), a novel method that is suitable for both generative modeling and distribution transfer tasks. Our approach is inspired by the physics of an electrical capacitor. We place source and target distributions on the capacitor plates and assign them positive and negative charges, respectively. We then learn the electrostatic field of the capacitor using a neural network approximator. To map the distributions to each other, we start at one plate of the capacitor and move the samples along the learned electrostatic field lines until they reach the other plate. We theoretically justify that this approach provably yields the distribution transfer. In practice, we demonstrate the performance of our EFM in toy and image data experiments.

However, magnetometer data often suffer from disturbances caused by satellite dynamics, onboard instrument interference, and environmental noise. For instance, changes in satellite orientation can lead to anomalies in magnetic field measurements due to interference from electric currents within the satellite's instruments. These disturbances necessitate careful data correction to ensure the accuracy and reliability of measurements. Traditional correction methods rely heavily on human expertise and are rooted in well established physical and mathematical principles. While these methods have proven effective, they are inherently limited by their long processing times and delays in real time prediction [7] [6] [4] [2] [1]. In contrast, machine learning models, though rarely applied in this field, offer strong predictive capabilities and the potential for faster computations. This study seeks to address these limitations by combining the strengths of traditional correction methods with the adaptability and efficiency of machine learning models, thereby improving timeliness while ensuring both physical consistency and improved real time performance. This study bridges the gap between data driven models and physics based understanding by integrating Maxwell's equations into the neural network architecture as physical information. The key innovations are: 1 arXiv:2501.00020v3

KEYWORDS Physics-informed neural network, Capacitive sensor, Simulation, Surrogate model, Maxwell's equations ABSTRACT Maxwell's equations are the fundamental equations for understanding electric and magnetic field interactions and play a crucial role in designing and optimizing sensor systems like capacitive touch sensors, which are widely prevalent in automotive switches and smartphones. This paper introduces a novel approach using a Physics-Informed Neural Network (PINN) based surrogate model to accelerate the design process. The PINN model solves the governing electrostatic equations describing the interaction between a finger and a capacitive sensor. Inputs include spatial coordinates from a 3D domain encompassing the finger, sensor, and PCB, along with finger distances. The learned model thus serves as a surrogate sensor model on which inference can be carried out in seconds for different experimental setups without the need to run simulations. Efficacy results evaluated on unseen test cases demonstrate the significant potential of PINNs in accelerating the development and design optimization of capacitive touch sensors.