A government spokesperson said: "We recognise that data centres face sustainability challenges such as energy demands and water use - that's why AI Growth Zones are designed to attract investment in areas where existing energy and water infrastructure is already in place." In addition, recent changes made by the water regulator Ofwat would "unlock 104bn of spending by water companies" in the next five years. The data centre industry argues that modern sites are already more efficient. Alternative cooling methods which do not require much water, such as free air cooling and dry cooling, are evolving. Closed-loop cooling, which involves reusing water, will be deployed in Microsoft's new data centres in Phoenix and Wisconsin.

Tech companies should be required by law to report the energy and water consumption for their data centres, as the boom in AI risks causing irreparable damage to the environment, experts have said. AI is growing at a rate unparalleled by other energy systems, bringing heightened environmental risk, a report by the National Engineering Policy Centre (NEPC) said. The report calls for the UK government to make tech companies submit mandatory reports on their energy and water consumption and carbon emissions in order to set conditions in which data centres are designed to use fewer vital resources. "In recent years advances in AI systems and services have largely been driven by a race for size and scale, demanding increasing amounts of computational power," said Prof Tom Rodden, the pro-vice-chancellor of research and knowledge exchange at the University of Nottingham, who was a member of the NEPC working group that delivered the study. "As a result, AI systems and services are growing at a rate unparalleled by other high-energy systems – and generally without much regard for resource efficiency. This is a dangerous trend, and we face a real risk that our development, deployment and use of AI could do irreparable damage to the environment."

We introduce a novel molecular representation through Algebraic Data Types (ADTs) - composite data structures formed through the combination of simpler types that obey algebraic laws. By explicitly considering how the datatype of a representation constrains the operations which may be performed, we ensure meaningful inference can be performed over generative models (programs with sample} and score operations). This stands in contrast to string-based representations where string-type operations may only indirectly correspond to chemical and physical molecular properties, and at worst produce nonsensical output. The ADT presented implements the Dietz representation for molecular constitution via multigraphs and bonding systems, and uses atomic coordinate data to represent 3D information and stereochemical features. This creates a general digital molecular representation which surpasses the limitations of the string-based representations and the 2D-graph based models on which they are based. In addition, we present novel support for quantum information through representation of shells, subshells, and orbitals, greatly expanding the representational scope beyond current approaches, for instance in Molecular Orbital theory. The framework's capabilities are demonstrated through key applications: Bayesian probabilistic programming is demonstrated through integration with LazyPPL, a lazy probabilistic programming library; molecules are made instances of a group under rotation, necessary for geometric learning techniques which exploit the invariance of molecular properties under different representations; and the framework's flexibility is demonstrated through an extension to model chemical reactions. After critiquing previous representations, we provide an open-source solution in Haskell - a type-safe, purely functional programming language.

Although deep models have been widely explored in solving partial differential equations (PDEs), previous works are primarily limited to data only with up to tens of thousands of mesh points, far from the million-point scale required by industrial simulations that involve complex geometries. In the spirit of advancing neural PDE solvers to real industrial applications, we present Transolver++, a highly parallel and efficient neural solver that can accurately solve PDEs on million-scale geometries. Building upon previous advancements in solving PDEs by learning physical states via Transolver, Transolver++ is further equipped with an extremely optimized parallelism framework and a local adaptive mechanism to efficiently capture eidetic physical states from massive mesh points, successfully tackling the thorny challenges in computation and physics learning when scaling up input mesh size. Transolver++ increases the single-GPU input capacity to million-scale points for the first time and is capable of continuously scaling input size in linear complexity by increasing GPUs. Experimentally, Transolver++ yields 13% relative promotion across six standard PDE benchmarks and achieves over 20% performance gain in million-scale high-fidelity industrial simulations, whose sizes are 100$\times$ larger than previous benchmarks, covering car and 3D aircraft designs.

Drift vehicle control offers valuable insights to support safe autonomous driving in extreme conditions, which hinges on tracking a particular path while maintaining the vehicle states near the drift equilibrium points (DEP). However, conventional tracking methods are not adaptable for drift vehicles due to their opposite steering angle and yaw rate. In this paper, we propose an adaptive path tracking (APT) control method to dynamically adjust drift states to follow the reference path, improving the commonly utilized predictive path tracking methods with released computation burden. Furthermore, existing control strategies necessitate a precise system model to calculate the DEP, which can be more intractable due to the highly nonlinear drift dynamics and sensitive vehicle parameters. To tackle this problem, an adaptive learning-based model predictive control (ALMPC) strategy is proposed based on the APT method, where an upper-level Bayesian optimization is employed to learn the DEP and APT control law to instruct a lower-level MPC drift controller. This hierarchical system architecture can also resolve the inherent control conflict between path tracking and drifting by separating these objectives into different layers. The ALMPC strategy is verified on the Matlab-Carsim platform, and simulation results demonstrate its effectiveness in controlling the drift vehicle to follow a clothoid-based reference path even with the misidentified road friction parameter.

High-performance computing and modern numerical algorithms have made high-fidelity fluid-thermal analysis tractable in geometries of ever increasing complexity. Despite continued advances in these areas, direct numerical (DNS), large eddy simulation (LES), and even unsteady Reynolds-averaged Navier-Stokes (URANS) simulations of turbulent thermal transport remain too costly for routine analysis and design of thermal-hydraulic systems, where hundreds of cases must be considered. Reduced order models (ROMs) offer a promising alternative by leveraging expensive high-fidelity simulations (referred to as full order models or FOMs) to first extract a low-dimensional basis that captures the principal features of the underlying flow fields, and then construct computational models whose dimensions are orders of magnitude lower than the FOM dimension. In the numerical simulation of fluid flows, Galerkin ROMs (G-ROMs), which use data-driven basis functions in a Galerkin framework, have provided efficient and accurate approximations of laminar flows, such as the two-dimensional flow past a circular cylinder at low Reynolds numbers [1, 2]. However, turbulent flows are notoriously hard for the standard G-ROM. Indeed, to capture the complex dynamics, a large number [3] of ROM basis functions is required, which yields high-dimensional ROMs that cannot be used in realistic applications. Thus, computationally efficient, low-dimensional ROMs are used instead. Unfortunately, these ROMs are inaccurate since the ROM basis functions that were not used to build the G-ROM have an important role in dissipating the energy from the system [4].

Solving complex planning problems requires Large Language Models (LLMs) to explicitly model the state transition to avoid rule violations, comply with constraints, and ensure optimality-a task hindered by the inherent ambiguity of natural language. To overcome such ambiguity, Planning Domain Definition Language (PDDL) is leveraged as a planning abstraction that enables precise and formal state descriptions. With PDDL, we can generate a symbolic world model where classic searching algorithms, such as A*, can be seamlessly applied to find optimal plans. However, directly generating PDDL domains with current LLMs remains an open challenge due to the lack of PDDL training data. To address this challenge, we propose to scale up the test-time computation of LLMs to enhance their PDDL reasoning capabilities, thereby enabling the generation of high-quality PDDL domains. Specifically, we introduce a simple yet effective algorithm, which first employs a Best-of-N sampling approach to improve the quality of the initial solution and then refines the solution in a fine-grained manner with verbalized machine learning. Our method outperforms o1-mini by a considerable margin in the generation of PDDL domain, achieving over 50% success rate on two tasks (i.e., generating PDDL domains from natural language description or PDDL problems). This is done without requiring additional training. By taking advantage of PDDL as state abstraction, our method is able to outperform current state-of-the-art methods on almost all competition-level planning tasks.

Nature is pervaded with oscillatory behavior. In networks of coupled oscillators patterns can arise when the system synchronizes to an external input. Hence, these networks provide processing and memory of input. We present a universal framework for harnessing oscillator networks as computational resource. This reservoir computing framework is introduced by the ubiquitous model for phase-locking, the Kuramoto model. We force the Kuramoto model by a nonlinear target-system, then after substituting the target-system with a trained feedback-loop it emulates the target-system. Our results are two-fold. Firstly, the trained network inherits performance properties of the Kuramoto model, where all-to-all coupling is performed in linear time with respect to the number of nodes and parameters for synchronization are abundant. Secondly, the learning capabilities of the oscillator network can be explained using Kuramoto model's order parameter. This work provides the foundation for utilizing nature's oscillator networks as a new class of information processing systems.

Connected and Automated Vehicle (CAV) research has gained traction in the last decade due to significant advancements in perception, navigation, communication, and control functions. Accurate and reliable position information is needed to meet the requirements of CAV applications, especially when safety is concerned. With the advent of various perception sensors (e.g. camera, LiDAR, etc.), the vehicular positioning system has improved both in accuracy and robustness. Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) based cooperative positioning can improve the accuracy of the position estimates, but the integrity risks involved in multi-sensor fusion in a cooperative environment have not yet been fully explored. This paper reviews existing research in the field of positioning Integrity Monitoring (IM) and identifies various research gaps. Particular attention has been placed on identifying research that highlights cooperative IM methods. This analysis helps pave the way for the development of new IM frameworks for cooperative positioning solutions in the future.

This study introduces a hybrid machine learning-based scale-bridging framework for predicting the permeability of fibrous textile structures. By addressing the computational challenges inherent to multiscale modeling, the proposed approach evaluates the efficiency and accuracy of different scale-bridging methodologies combining traditional surrogate models and even integrating physics-informed neural networks (PINNs) with numerical solvers, enabling accurate permeability predictions across micro- and mesoscales. Four methodologies were evaluated: Single Scale Method (SSM), Simple Upscaling Method (SUM), Scale-Bridging Method (SBM), and Fully Resolved Model (FRM). SSM, the simplest method, neglects microscale permeability and exhibited permeability values deviating by up to 150\% of the FRM model, which was taken as ground truth at an equivalent lower fiber volume content. SUM improved predictions by considering uniform microscale permeability, yielding closer values under similar conditions, but still lacked structural variability. The SBM method, incorporating segment-based microscale permeability assignments, showed significant enhancements, achieving almost equivalent values while maintaining computational efficiency and modeling runtimes of ~45 minutes per simulation. In contrast, FRM, which provides the highest fidelity by fully resolving microscale and mesoscale geometries, required up to 270 times more computational time than SSM, with model files exceeding 300 GB. Additionally, a hybrid dual-scale solver incorporating PINNs has been developed and shows the potential to overcome generalization errors and the problem of data scarcity of the data-driven surrogate approaches. The hybrid framework advances permeability modelling by balancing computational cost and prediction reliability, laying the foundation for further applications in fibrous composite manufacturing.