This paper presents the Relaxed Continuous-Time Actor-critic (RCTAC) algorithm, a method for finding the nearly optimal policy for nonlinear continuous-time (CT) systems with known dynamics and infinite horizon, such as the path-tracking control of vehicles. RCTAC has several advantages over existing adaptive dynamic programming algorithms for CT systems. It does not require the ``admissibility" of the initialized policy or the input-affine nature of controlled systems for convergence. Instead, given any initial policy, RCTAC can converge to an admissible, and subsequently nearly optimal policy for a general nonlinear system with a saturated controller. RCTAC consists of two phases: a warm-up phase and a generalized policy iteration phase. The warm-up phase minimizes the square of the Hamiltonian to achieve admissibility, while the generalized policy iteration phase relaxes the update termination conditions for faster convergence. The convergence and optimality of the algorithm are proven through Lyapunov analysis, and its effectiveness is demonstrated through simulations and real-world path-tracking tasks.

Nowadays, the ever-increasing urbanization and industrialization has led to a growing of water demand and a decrease in water supply and resources, thus creating a huge divergence between demand and supply. Therefore, water resources can play an important role in regional socio-economic and environmental development [Setegn, 2015]. The effective distribution of water resources in both civil and industry life indicates the levels of urban sustainability and social inclusiveness. Proper water distribution and forecasting can act as a baseline for achieving optimal resource allocation and mitigating the gap between supply and demand, thus improving operations, planning and management. In Greece, the recent years, the need for accurate water demand forecasting has become particularly important [Bithas and Chrysostomos, 2006]. The systematically extraction of non-renewable ground water, the insertion of chemicals for water purification, the drought caused by climate changes in the region of the Mediterranean and the sudden rise of water demand due to the increase of refugees and migrants has created many environmental issues on the quantity and quality of the water resources as well as previously unseen socio-economic and political problems. Therefore, an accurate forecasting of water consumption can be a decisive factor for proper planning, management and optimization. Water consumption data are seen as time series, since a measurement of water consumption levels is taken periodically (weekly, monthly, quarterly).

Short-term forecasting of solar photovoltaic energy (PV) production is important for powerplant management. Ideally these forecasts are equipped with error bars, so that downstream decisions can account for uncertainty. To produce predictions with error bars in this setting, we consider Gaussian processes (GPs) for modelling and predicting solar photovoltaic energy production in the UK. A standard application of GP regression on the PV timeseries data is infeasible due to the large data size and non-Gaussianity of PV readings. However, this is made possible by leveraging recent advances in scalable GP inference, in particular, by using the state-space form of GPs, combined with modern variational inference techniques. The resulting model is not only scalable to large datasets but can also handle continuous data streams via Kalman filtering.

Physics-informed machine learning (physics-ML) is transforming high-performance computing (HPC) simulation workflows across disciplines, including computational fluid dynamics, structural mechanics, and computational chemistry. Because of its broad applications, physics-ML is well suited for modeling physical systems and deploying digital twins across industries ranging from manufacturing to climate sciences. NVIDIA Modulus is a state-of-the-art physics-ML platform that blends physics with deep learning training data to build high-fidelity, parameterized surrogate models with near-real-time latency. The surrogate models built using NVIDIA Modulus help a wide range of solutions including weather forecasting, reducing power plant greenhouse gasses, and accelerating clean energy transitions. NVIDIA Modulus customer success stories are proving the platform's incredible utility across industries.

ChatGPT, Bing Chat, and Bard promise to transform your life using the power of artificial intelligence, through AI conversations that can inform, amuse, and educate you--just like a human being. But how good are these new AI chatbots, really? We tested them to find out. We asked all three AIs a variety of different questions: some that expanded upon general search topics, some that demanded an opinion, logic puzzles, even code--and then asked them to be more creative, such as by writing an alternate, better ending to Game of Thrones and a Seinfeld scene with a special guest. We've included all of their answers, or as much as them as we could provide, and we'll let you decide for yourself.

Deep-sea mining would cause "extensive and irreversible" damage to sensitive habitats.NOAA This story was originally published by the Guardian and is reproduced here as part of the Climate Desk collaboration. An investigation by conservationists has found evidence that deep-seabed mining of rare minerals could cause "extensive and irreversible" damage to the planet. The report, published on Monday by the international wildlife charity Fauna & Flora, adds to the growing controversy that surrounds proposals to sweep the ocean floor of rare minerals that include cobalt, manganese and nickel. Mining companies want to exploit these deposits--which are crucial to the alternative energy sector--because land supplies are running low, they say.

The field of machine learning has rapidly advanced the state of the art in many fields of science and engineering, including experimental fluid dynamics, which is one of the original big-data disciplines. This perspective will highlight several aspects of experimental fluid mechanics that stand to benefit from progress advances in machine learning, including: 1) augmenting the fidelity and quality of measurement techniques, 2) improving experimental design and surrogate digital-twin models and 3) enabling real-time estimation and control. In each case, we discuss recent success stories and ongoing challenges, along with caveats and limitations, and outline the potential for new avenues of ML-augmented and ML-enabled experimental fluid mechanics.

Partial differential equations (PDEs) have been a cornerstone of mathematical physics and engineering design for over 250 years, since the introduction of the one-dimensional wave equation by d'Alembert in 1752 [20]. PDEs provide a formal mathematical infrastructure for relating how quantities of interest change in several variables, typically space and time. As such, PDEs provide a foundational description of the governing equations of many canonical spatio-temporal physical systems, including electrodynamics, quantum mechanics, fluid mechanics, heat transfer, etc. Today, nearly every aspect of our engineered world is based in some way on the predictive capability of PDEs, from structural modeling of buildings and bridges, to the design of aircraft and other vehicles, to the thermal and electromagnetic management systems in modern portable electronics.

We are currently in a great era for researchers and scientists studying and developing in the field of complex systems. Half of the physics Nobel prize of 2021 was awarded to the physicist Giorgio Parisi for his contributions to the theory of complex systems [9] and the other half to two meteorologists Syukuro Manabe and Klaus Hasselmann to the modeling of the Earth's climate [10]. Parisi has made significant contributions to the literature on complex systems, including areas such as spin glass [11, 12, 13], stochastic resonance [14], surface growth [15], multifractality [16], and bird flocking [17].

We present an approach for designing swarm-based optimizers for the global optimization of expensive black-box functions. In the proposed approach, the problem of finding efficient optimizers is framed as a reinforcement learning problem, where the goal is to find optimization policies that require a few function evaluations to converge to the global optimum. The state of each agent within the swarm is defined as its current position and function value within a design space and the agents learn to take favorable actions that maximize reward, which is based on the final value of the objective function. The proposed approach is tested on various benchmark optimization functions and compared to the performance of other global optimization strategies. Furthermore, the effect of changing the number of agents, as well as the generalization capabilities of the trained agents are investigated. The results show superior performance compared to the other optimizers, desired scaling when the number of agents is varied, and acceptable performance even when applied to unseen functions. On a broader scale, the results show promise for the rapid development of domain-specific optimizers.