With the increasing amount of renewable energy in the grid, long-term wind power forecasting for multiple decades becomes more critical. In these long-term forecasts, climate data is essential as it allows us to account for climate change. Yet the resolution of climate models is often very coarse. In this paper, we show that by including turbine locations when downscaling with Gaussian Processes, we can generate valuable aggregate wind power predictions despite the low resolution of the CMIP6 climate models. This work is a first step towards multi-decadal turbine-location-aware wind power forecasting using global climate model output.

Offline reinforcement learning (RL) is a promising approach for many control applications but faces challenges such as limited data coverage and value function overestimation. In this paper, we propose an implicit actor-critic (iAC) framework that employs optimization solution functions as a deterministic policy (actor) and a monotone function over the optimal value of optimization as a critic. By encoding optimality in the actor policy, we show that the learned policies are robust to the suboptimality of the learned actor parameters via the exponentially decaying sensitivity (EDS) property. We obtain performance guarantees for the proposed iAC framework and show its benefits over general function approximation schemes. Finally, we validate the proposed framework on two real-world applications and show a significant improvement over state-of-the-art (SOTA) offline RL methods.

Every time someone uses ChatGPT to write an essay, create an image or advise them on planning their day, the environment pays a price. A query on the chatbot that uses artificial intelligence is estimated to require at least 10 times more electricity than a standard search on Google. If all Google searches similarly used generative AI, they might consume as much electricity as a country the size of Ireland, calculates Alex de Vries, the founder of Digiconomist, a website that aims to expose the unintended consequences of digital trends. Yet someone using ChatGPT or another artificial intelligence application has no way of knowing how much power their questions will consume as they are processed in the tech companies' enormous data centers. De Vries said the skyrocketing energy demand of AI technologies will no doubt require the world to burn more climate-warming oil, gas and coal.

Traditional numerical simulation methods require substantial computational resources to accurately determine the complete nonlinear thermoacoustic response of flames to various perturbation frequencies and amplitudes. In this paper, we have developed deep learning algorithms that can construct a comprehensive flame nonlinear response from limited numerical simulation data. To achieve this, we propose using a frequency-sweeping data type as the training dataset, which incorporates a rich array of learnable information within a constrained dataset. To enhance the precision in learning flame nonlinear response patterns from the training data, we introduce a Dual-Path neural network. This network consists of a Chronological Feature Path and a Temporal Detail Feature Path. The Dual-Path network is specifically designed to focus intensively on the temporal characteristics of velocity perturbation sequences, yielding more accurate flame response patterns and enhanced generalization capabilities. Validations confirm that our approach can accurately model flame nonlinear responses, even under conditions of significant nonlinearity, and exhibits robust generalization capabilities across various test scenarios.

Miniaturized magnetic soft robots have shown extraordinary capabilities of contactless manipulation, complex path maneuvering, precise localization, and quick actuation, which have equipped them to cater to challenging biomedical applications such as targeted drug delivery, internal wound healing, and laparoscopic surgery. However, despite their successful fabrication by several different research groups, a thorough design strategy encompassing the optimized kinematic performance of the three fundamental biomimetic swimming modes at miniaturized length scales has not been reported till now. Here, we resolve this by designing magnetic soft robotic swimmers (MSRSs) from the class of helical and undulatory low Reynolds number (Re) swimmers using a fully coupled, experimentally calibrated computational fluid dynamics model. We study (and compare) their swimming performance, and report their steady-state swimming speed for different non-dimensional numbers that capture the competition by magnetic loading, non-linear elastic deformation and viscous solid-fluid coupling. We investigate their stability for different initial spatial orientations to ensure robustness during real-life applications. Our results show that the helical 'finger-shaped' swimmer is, by far, the fastest low Re swimmer in terms of body lengths per cycle, but that the undulatory 'carangiform' swimmer proved to be the most versatile, bi-directional swimmer with maximum stability.

Power grid operators depend on accurate and reliable energy forecasts, aiming to minimize cases of extreme errors, as these outliers are particularly challenging to manage during operation. Incorporating planning information - such as known data about users' future behavior or scheduled events - has the potential to significantly enhance the accuracy and specificity of forecasts. Although there have been attempts to integrate such expected future behavior, these efforts consistently rely on conventional regression models to process this information. These models often lack the flexibility and capability to effectively incorporate both dynamic, forward-looking contextual inputs and historical data. To address this challenge, we conceptualize this combined forecasting and regression challenge as a sequence-to-sequence modeling problem and demonstrate, with three distinct models, that our contextually enhanced transformer models excel in this task. By leveraging schedule-based contextual information from the Swiss railway traction network, our proposed method significantly improved the average forecasting accuracy of nationwide railway energy consumption. Specifically, enhancing the transformer models with contextual information resulted in an average reduction of mean absolute error by 40.6%, whereas other state-of-the-art methods did not demonstrate any significant improvement. Despite extensive research efforts to forecast energy usage in electrical grids, operators still encounter significant outliers when faced with unexpected scenarios, with relative errors occasionally exceeding 50%, posing considerable operational challenges. Our research reveals that a critical limitation of current forecasting approaches is their over-reliance on trends and periodic patterns from past observations. We challenge this conventional focus on historical data as the primary source for energy forecasts and advocate for integrating contextual information about the expected future, such as anticipated user behavior and scheduled events. By incorporating this expected future information, we significantly improve the accuracy and specificity of load forecasts. This approach proves crucial for improving forecasting capabilities, and this methodology can be broadly applied to other domains where similar planning information is available. Electrical energy distinguishes itself from other traded commodities because its transmission follows the power flow equations Kundur (2012); Pagnier & Chertkov (2021). These equations require that the amount of energy consumed (demand) must always match the amount of energy produced (supply) to maintain the stability of the power grid Ullah et al. (2021). In compliance with these laws, grid operators collaborate closely with energy traders to ensure frequency synchronization and voltage stabilization, preventing damage to infrastructure and grid-connected assets Klyuev et al. (2022).

An SCR that is assessed as relevant to safety goes through extra scrutiny to maintain personnel safety at the nuclear station. The current method of SCR classification is a manual one that involves human evaluators to examine multiple SCRs every week. These records, which may be submitted by any employee, cover a broad spectrum of events and undergo management review to determine an appropriate reaction. If an SCR is deemed relevant to safety, it undergoes further examination by the Health and Safety department and is documented in a specialized database. The SCR database encompasses a range of occurrences, from equipment malfunctions and delays in material delivery to staff missing training sessions, making it cumbersome for the Health and Safety department to sift through each SCR to identify safety-related items before transferring pertinent details into their safety tracking system. The aim of this project is to develop a machine learning classifier to automatically differentiate between safety-related and non-safety-related SCRs. While this tool is not intended to supplant human assessment, it will serve as an additional layer of scrutiny and facilitate the swift review of safetyrelated SCRs by triggering a pipeline that copies all relevant data into the safety system for final human verification.

Human activity recognition (HAR) holds immense potential for transforming health and fitness monitoring, yet challenges persist in achieving personalized outcomes and sustainability for on-device continuous inferences. This work introduces a wrist-worn smart band designed to address these challenges through a novel combination of on-device TinyML-driven computing and cloud-enabled auto-deployment. Leveraging inertial measurement unit (IMU) sensors and a customized 1D Convolutional Neural Network (CNN) for personalized HAR, users can tailor activity classes to their unique movement styles with minimal calibration. By utilising TinyML for local computations, the smart band reduces the necessity for constant data transmission and radio communication, which in turn lowers power consumption and reduces carbon footprint. This method also enhances the privacy and security of user data by limiting its transmission. Through transfer learning and fine-tuning on user-specific data, the system achieves a 37\% increase in accuracy over generalized models in personalized settings. Evaluation using three benchmark datasets, WISDM, PAMAP2, and the BandX demonstrates its effectiveness across various activity domains. Additionally, this work presents a cloud-supported framework for the automatic deployment of TinyML models to remote wearables, enabling seamless customization and on-device inference, even with limited target data. By combining personalized HAR with sustainable strategies for on-device continuous inferences, this system represents a promising step towards fostering healthier and more sustainable societies worldwide.

This paper examines the application of ChatGPT, a large language model (LLM), for question-and-answer (Q&A) tasks in the highly specialized field of nuclear data. The primary focus is on evaluating ChatGPT's performance on a curated test dataset, comparing the outcomes of a standalone LLM with those generated through a Retrieval Augmented Generation (RAG) approach. LLMs, despite their recent advancements, are prone to generating incorrect or 'hallucinated' information, which is a significant limitation in applications requiring high accuracy and reliability. This study explores the potential of utilizing RAG in LLMs, a method that integrates external knowledge bases and sophisticated retrieval techniques to enhance the accuracy and relevance of generated outputs. In this context, the paper evaluates ChatGPT's ability to answer domain-specific questions, employing two methodologies: A) direct response from the LLM, and B) response from the LLM within a RAG framework. The effectiveness of these methods is assessed through a dual mechanism of human and LLM evaluation, scoring the responses for correctness and other metrics. The findings underscore the improvement in performance when incorporating a RAG pipeline in an LLM, particularly in generating more accurate and contextually appropriate responses for nuclear domain-specific queries. Additionally, the paper highlights alternative approaches to further refine and improve the quality of answers in such specialized domains.

We propose a novel learning framework for Koopman operator of nonlinear dynamical systems that is informed by the governing equation and guarantees long-time stability and robustness to noise. In contrast to existing frameworks where either ad-hoc observables or blackbox neural networks are used to construct observables in the extended dynamic mode decomposition (EDMD), our observables are informed by governing equations via Polyflow. To improve the noise robustness and guarantee long-term stability, we designed a stable parameterization of the Koopman operator together with a progressive learning strategy for roll-out recurrent loss. To further improve model performance in the phase space, a simple iterative strategy of data augmentation was developed. Numerical experiments of prediction and control of classic nonlinear systems with ablation study showed the effectiveness of the proposed techniques over several state-of-the-art practices.