Among a variety of other considerations, energy efficiency is a major focus for the Union's ultimate decarbonization. This makes high energy efficiency a critical priority for all energy sectors, particularly the residential sector [2], which occupies more than a quarter of the Union's total final energy consumption. Energy decentralization has emerged as one of the most popular contemporary research topic in this domain as a mean for increasing energy efficiency [3]. With the growing usage of Information and Communication Technologies (ICT) in the Internet of Things (IoT) sector, data on household energy consumption and production (HECP) may now be generated in a decentralized manner, for example, from an electric vehicle, a heat pump, or home appliances. Due to the range and granularity of data-generating devices, a new generation of smart household energy systems is geared toward decentralization and has the potential to considerably assist in the transition to a sustainable energy future [4, 5]. On the other hand, evaluating household energy data is getting increasingly difficult as a result of various smart devices interacting and forming a complex energy flow data network [6, 7]. Decentralized energy systems are often paired with research into data-driven technologies (e.g. machine learning) for opti-2 mizing the systems based on the massive ocean of incoming data in order to manage the inherent risk associated with energy usage's intermittent and unpredictable nature and achieve energy sustainability, including cost reduction, emission reduction, and energy efficiency. However, most of those technologies are developed for project-specific decentralized data (i.e.

Smart energy networks provide for an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for deep decarbonisation of energy production. However, given the variability of the renewables as well as the energy demand, it is imperative to develop effective control and energy storage schemes to manage the variable energy generation and achieve desired system economics and environmental goals. In this paper, we introduce a hybrid energy storage system composed of battery and hydrogen energy storage to handle the uncertainties related to electricity prices, renewable energy production and consumption. We aim to improve renewable energy utilisation and minimise energy costs and carbon emissions while ensuring energy reliability and stability within the network. To achieve this, we propose a multi-agent deep deterministic policy gradient approach, which is a deep reinforcement learning-based control strategy to optimise the scheduling of the hybrid energy storage system and energy demand in real-time. The proposed approach is model-free and does not require explicit knowledge and rigorous mathematical models of the smart energy network environment. Simulation results based on real-world data show that: (i) integration and optimised operation of the hybrid energy storage system and energy demand reduces carbon emissions by 78.69%, improves cost savings by 23.5% and renewable energy utilisation by over 13.2% compared to other baseline models and (ii) the proposed algorithm outperforms the state-of-the-art self-learning algorithms like deep-Q network.

A unique convergence of three mega trends helped bring Artificial Intelligence out of academia and made it ubiquitous in everyday applications – big data, cloud compute, and advanced algorithms. Today, AI has fundamentally changed how software is written and it is integrated into daily digital experiences, such as writing emails, searching the web, buying clothing, searching and listening to music, and building websites. Somewhat slower, though, has been the spread of AI in the global infrastructure systems of manufacturing, transportation, aviation, power generation, financial services, and other industries. These very real challenges make applying the same AI techniques that revolutionized internet search, reading invoices, translating languages, and holding conversations inapplicable as-is to specialized domains. Practitioners of AI in industry are realizing that conventional supervised machine learning approaches and large scale models from academia and research often fail in specialized domains, making the operationalization of big data in the commercial enterprises very difficult.

When a disaster strikes Indonesia, residents may well log onto social media before taking shelter. Posts tagging the PetaBencana initiative with a reference to the disaster – be it due to a flood, earthquake, or volcanic eruption – will prompt a chatbot to appear with a link to the PetaBencana platform. Users can then share their location, photos of any visible damage, and details like flood depth. Indonesian government agencies then validate these crowdsourced situation reports, using the data to coordinate emergency response measures. Residents can also consult the resulting collaborative map in real time to make informed decisions about their safety and security.

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Uncertainty quantification (UQ) in machine learning is currently drawing increasing research interest, driven by the rapid deployment of deep neural networks across different fields, such as computer vision, natural language processing, and the need for reliable tools in risk-sensitive applications. Recently, various machine learning models have also been developed to tackle problems in the field of scientific computing with applications to computational science and engineering (CSE). Physics-informed neural networks and deep operator networks are two such models for solving partial differential equations and learning operator mappings, respectively. In this regard, a comprehensive study of UQ methods tailored specifically for scientific machine learning (SciML) models has been provided in [45]. Nevertheless, and despite their theoretical merit, implementations of these methods are not straightforward, especially in large-scale CSE applications, hindering their broad adoption in both research and industry settings. In this paper, we present an open-source Python library (https://github.com/Crunch-UQ4MI), termed NeuralUQ and accompanied by an educational tutorial, for employing UQ methods for SciML in a convenient and structured manner. The library, designed for both educational and research purposes, supports multiple modern UQ methods and SciML models. It is based on a succinct workflow and facilitates flexible employment and easy extensions by the users. We first present a tutorial of NeuralUQ and subsequently demonstrate its applicability and efficiency in four diverse examples, involving dynamical systems and high-dimensional parametric and time-dependent PDEs.

Assessment of existing buildings' recycling costs often requires a destructive method to look through its building elements like walls and floors. In order to answer what materials comprise a wall, one often has to obtain an explicit overview of the cross-section - either by drilling or carving out a piece. Ground-penetrating radar (GPR) presents the way to examine the walls without a destructive invasive process. GeoRadar has been successfully utilized in other fields, such as archaeology Zhao et al. [2013], seismology Zheng et al. [2019] and civil engineering for non-destructive examination Morris et al. [2019]. In order to identify materials in layered structures, one could refer to the research for a similar problem.

Wasserstein-Fisher-Rao (WFR) distance is a family of metrics to gauge the discrepancy of two Radon measures, which takes into account both transportation and weight change. Spherical WFR distance is a projected version of WFR distance for probability measures so that the space of Radon measures equipped with WFR can be viewed as metric cone over the space of probability measures with spherical WFR. Compared to the case for Wasserstein distance, the understanding of geodesics under the spherical WFR is less clear and still an ongoing research focus. In this paper, we develop a deep learning framework to compute the geodesics under the spherical WFR metric, and the learned geodesics can be adopted to generate weighted samples. Our approach is based on a Benamou-Brenier type dynamic formulation for spherical WFR. To overcome the difficulty in enforcing the boundary constraint brought by the weight change, a Kullback-Leibler (KL) divergence term based on the inverse map is introduced into the cost function. Moreover, a new regularization term using the particle velocity is introduced as a substitute for the Hamilton-Jacobi equation for the potential in dynamic formula. When used for sample generation, our framework can be beneficial for applications with given weighted samples, especially in the Bayesian inference, compared to sample generation with previous flow models.

There is an increasing demand for using Unmanned Aerial Vehicle (UAV), known as drones, in different applications such as packages delivery, traffic monitoring, search and rescue operations, and military combat engagements. In all of these applications, the UAV is used to navigate the environment autonomously - without human interaction, perform specific tasks and avoid obstacles. Autonomous UAV navigation is commonly accomplished using Reinforcement Learning (RL), where agents act as experts in a domain to navigate the environment while avoiding obstacles. Understanding the navigation environment and algorithmic limitations plays an essential role in choosing the appropriate RL algorithm to solve the navigation problem effectively. Consequently, this study first identifies the main UAV navigation tasks and discusses navigation frameworks and simulation software. Next, RL algorithms are classified and discussed based on the environment, algorithm characteristics, abilities, and applications in different UAV navigation problems, which will help the practitioners and researchers select the appropriate RL algorithms for their UAV navigation use cases. Moreover, identified gaps and opportunities will drive UAV navigation research.

The purpose of this study is to introduce ANN-based software for the fast evaluation of rotordynamics in the context of robust and integrated design. It is based on a surrogate model made of ensembles of artificial neural networks running in a Bokeh web application. The use of a surrogate model has sped up the computation by three orders of magnitude compared to the current models. ARRID offers fast performance information, including the effect of manufacturing deviations. As such, it helps the designer to make optimal design choices early in the design process. The designer can manipulate the parameters of the design and the operating conditions to obtain performance information in a matter of seconds.