Abandoning fossil fuels and adopting lower-carbon technologies are our best options for warding off the accelerating threat of climate change. And access to rare earth elements, key ingredients in many of these technologies, will partly determine which countries will meet their goals for lowering emissions. Some nations, including the US, are increasingly worried about whether the supply of those elements will remain stable. As a result, scientists and companies alike are intent on increasing access and improving sustainability by exploring secondary or unconventional sources. This story is from the next magazine edition of MIT Technology Review, set to go live on January 8--and it's all about innovation.

We present a data-driven model for ground-motion synthesis using a Generative Adversarial Neural Operator (GANO) that combines recent advancements in machine learning and open access strong motion data sets to generate three-component acceleration time histories conditioned on moment magnitude ($M$), rupture distance ($R_{rup}$), time-average shear-wave velocity at the top $30m$ ($V_{S30}$), and tectonic environment or style of faulting. We use Neural Operators, a resolution invariant architecture that guarantees that the model training is independent of the data sampling frequency. We first present the conditional ground-motion synthesis algorithm (referred to heretofore as cGM-GANO) and discuss its advantages compared to previous work. Next, we verify the cGM-GANO framework using simulated ground motions generated with the Southern California Earthquake Center (SCEC) Broadband Platform (BBP). We lastly train cGM-GANO on a KiK-net dataset from Japan, showing that the framework can recover the magnitude, distance, and $V_{S30}$ scaling of Fourier amplitude and pseudo-spectral accelerations. We evaluate cGM-GANO through residual analysis with the empirical dataset as well as by comparison with conventional Ground Motion Models (GMMs) for selected ground motion scenarios. Results show that cGM-GANO produces consistent median scaling with the GMMs for the corresponding tectonic environments. The largest misfit is observed at short distances due to the scarcity of training data. With the exception of short distances, the aleatory variability of the response spectral ordinates is also well captured, especially for subduction events due to the adequacy of training data. Applications of the presented framework include generation of risk-targeted ground motions for site-specific engineering applications.

This research addresses the challenge of executing multi-UAV survey missions over diverse terrains characterized by varying elevations. The approach integrates advanced two-dimensional ergodic search technique with model predictive control of UAV altitude and velocity. Optimization of altitude and velocity is performed along anticipated UAV ground routes, considering multiple objectives and constraints. This yields a flight regimen tailored to the terrain, as well as the motion and sensing characteristics of the UAVs. The proposed UAV motion control strategy is assessed through simulations of realistic search missions and actual terrain models. Results demonstrate the successful integration of model predictive altitude and velocity control with a two-dimensional potential field-guided ergodic search. Adjusting UAV altitudes to near-ideal levels facilitates the utilization of sensing ranges, thereby enhancing the effectiveness of the search. Furthermore, the control algorithm is capable of real-time computation, encouraging its practical application in real-world scenarios.

This study explores the potential of physics-informed neural networks (PINNs) for the realization of digital twins (DT) from various perspectives. First, various adaptive sampling approaches for collocation points are investigated to verify their effectiveness in the mesh-free framework of PINNs, which allows automated construction of virtual representation without manual mesh generation. Then, the overall performance of the data-driven PINNs (DD-PINNs) framework is examined, which can utilize the acquired datasets in DT scenarios. Its scalability to more general physics is validated within parametric Navier-Stokes equations, where PINNs do not need to be retrained as the Reynolds number varies. In addition, since datasets can be often collected from different fidelity/sparsity in practice, multi-fidelity DD-PINNs are also proposed and evaluated. They show remarkable prediction performance even in the extrapolation tasks, with $42\sim62\%$ improvement over the single-fidelity approach. Finally, the uncertainty quantification performance of multi-fidelity DD-PINNs is investigated by the ensemble method to verify their potential in DT, where an accurate measure of predictive uncertainty is critical. The DD-PINN frameworks explored in this study are found to be more suitable for DT scenarios than traditional PINNs from the above perspectives, bringing engineers one step closer to seamless DT realization.

This work aims at improving the energy efficiency of decentralized learning by optimizing the mixing matrix, which controls the communication demands during the learning process. Through rigorous analysis based on a state-of-the-art decentralized learning algorithm, the problem is formulated as a bi-level optimization, with the lower level solved by graph sparsification. A solution with guaranteed performance is proposed for the special case of fully-connected base topology and a greedy heuristic is proposed for the general case. Simulations based on real topology and dataset show that the proposed solution can lower the energy consumption at the busiest node by 54%-76% while maintaining the quality of the trained model.

This paper presents policy-based motion planning for robotic systems. The motion planning literature has been mostly focused on open-loop trajectory planning which is followed by tracking online. In contrast, we solve the problem of path planning and controller synthesis simultaneously by solving the related feedback control problem. We present a novel incremental policy (iPolicy) algorithm for motion planning, which integrates sampling-based methods and set-valued optimal control methods to compute feedback controllers for the robotic system. In particular, we use sampling to incrementally construct the state space of the system. Asynchronous value iterations are performed on the sampled state space to synthesize the incremental policy feedback controller. We show the convergence of the estimates to the optimal value function in continuous state space. Numerical results with various different dynamical systems (including nonholonomic systems) verify the optimality and effectiveness of iPolicy.

However, the rapid adoption of small-scale renewable at the edge of the grid makes the smart grid management process more complex and exposed to uncertainty related to renewable production [1, 2]. The digitization and decentralization principles bring to the forefront the energy demand flexibility as a key support to accommodate high shares of variable renewable energy [3, 4]. Leveraging local flexibility is possible to maintain a balance between supply and demand at lower costs using the energy assets of the citizens rather than the ones owned by the grid operator that are more expensive to operate [5-7]. The challenges are even more evident and difficult to tackle in the context of the increased adoption of electrical vehicles (EVs) [8]. In that respect grid management should closely cooperate and interact within a low latency context with EVs coordination and aggregation services to procure their energy scheduling flexibility to support local network balancing or to achieve self-sufficiency [9, 10]. However, EVs usage has several shortcomings such as the limited battery range, relatively short battery lifespan, averaging 10-20 years or up to 150,000 miles, and the lack of existing infrastructure for charging electric vehicles. Other major issues refer to the impact of EVs on the power grid, encompassing factors such as the rise in short-circuit currents, deviations in voltage levels beyond standard limits, and the potential impact on the lifespan of equipment due to increased power demand [11].

Split Federated Learning (SFL) has recently emerged as a promising distributed learning technology, leveraging the strengths of both federated learning and split learning. It emphasizes the advantages of rapid convergence while addressing privacy concerns. As a result, this innovation has received significant attention from both industry and academia. However, since the model is split at a specific layer, known as a cut layer, into both client-side and server-side models for the SFL, the choice of the cut layer in SFL can have a substantial impact on the energy consumption of clients and their privacy, as it influences the training burden and the output of the client-side models. Moreover, the design challenge of determining the cut layer is highly intricate, primarily due to the inherent heterogeneity in the computing and networking capabilities of clients. In this article, we provide a comprehensive overview of the SFL process and conduct a thorough analysis of energy consumption and privacy. This analysis takes into account the influence of various system parameters on the cut layer selection strategy. Additionally, we provide an illustrative example of the cut layer selection, aiming to minimize the risk of clients from reconstructing the raw data at the server while sustaining energy consumption within the required energy budget, which involve trade-offs. Finally, we address open challenges in this field. These directions represent promising avenues for future research and development.

Reliable and efficient trajectory optimization methods are a fundamental need for autonomous dynamical systems, effectively enabling applications including rocket landing, hypersonic reentry, spacecraft rendezvous, and docking. Within such safety-critical application areas, the complexity of the emerging trajectory optimization problems has motivated the application of AI-based techniques to enhance the performance of traditional approaches. However, current AI-based methods either attempt to fully replace traditional control algorithms, thus lacking constraint satisfaction guarantees and incurring in expensive simulation, or aim to solely imitate the behavior of traditional methods via supervised learning. To address these limitations, this paper proposes the Autonomous Rendezvous Transformer (ART) and assesses the capability of modern generative models to solve complex trajectory optimization problems, both from a forecasting and control standpoint. Specifically, this work assesses the capabilities of Transformers to (i) learn near-optimal policies from previously collected data, and (ii) warm-start a sequential optimizer for the solution of non-convex optimal control problems, thus guaranteeing hard constraint satisfaction. From a forecasting perspective, results highlight how ART outperforms other learning-based architectures at predicting known fuel-optimal trajectories. From a control perspective, empirical analyses show how policies learned through Transformers are able to generate near-optimal warm-starts, achieving trajectories that are (i) more fuel-efficient, (ii) obtained in fewer sequential optimizer iterations, and (iii) computed with an overall runtime comparable to benchmarks based on convex optimization.

Image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery. Current methods struggle to effectively consider land objects with diverse shapes and sizes. Additionally, the determination of segmentation scale parameters frequently adheres to a static and empirical doctrine, posing limitations on the segmentation of large-scale remote sensing images and yielding algorithms with limited interpretability. To address the above challenges, we propose a deep-learning-based region merging method dubbed DeepMerge to handle the segmentation of complete objects in large VHR images by integrating deep learning and region adjacency graph (RAG). This is the first method to use deep learning to learn the similarity and merge similar adjacent super-pixels in RAG. We propose a modified binary tree sampling method to generate shift-scale data, serving as inputs for transformer-based deep learning networks, a shift-scale attention with 3-Dimension relative position embedding to learn features across scales, and an embedding to fuse learned features with hand-crafted features. DeepMerge can achieve high segmentation accuracy in a supervised manner from large-scale remotely sensed images and provides an interpretable optimal scale parameter, which is validated using a remote sensing image of 0.55 m resolution covering an area of 5,660 km^2. The experimental results show that DeepMerge achieves the highest F value (0.9550) and the lowest total error TE (0.0895), correctly segmenting objects of different sizes and outperforming all competing segmentation methods.