Energy estimation is critical to impact identification on aerospace composites, where low-velocity impacts can induce internal damage that is undetectable at the surface. Current methodologies for energy prediction are often constrained by data sparsity, signal noise, complex feature interdependencies, non-linear dynamics, massive design spaces, and the ill-posed nature of the inverse problem. This study introduces a physics-informed framework that embeds domain knowledge into machine learning through a dedicated input space. The approach combines observational biases, which guide the design of physics-motivated features, with targeted feature selection to retain only the most informative indicators. Features are extracted from time, frequency, and time-frequency domains to capture complementary aspects of the structural response. A structured feature selection process integrating statistical significance, correlation filtering, dimensionality reduction, and noise robustness ensures physical relevance and interpretability. Exploratory data analysis further reveals domain-specific trends, yielding a reduced feature set that captures essential dynamic phenomena such as amplitude scaling, spectral redistribution, and transient signal behaviour. Together, these steps produce a compact set of energy-sensitive indicators with both statistical robustness and physical significance, resulting in impact energy predictions that remain interpretable and traceable to measurable structural responses. Using this optimised input space, a fully-connected neural network is trained and validated with experimental data from multiple impact scenarios, including pristine and damaged states. The resulting model demonstrates significantly improved impact energy prediction accuracy, reducing errors by a factor of three compared to conventional time-series techniques and purely data-driven models.

This paper presents a machine learning-based approach to estimate the energy consumption of virtual servers without access to physical power measurement interfaces. Using resource utilization metrics collected from guest virtual machines, we train a Gradient Boosting Regressor to predict energy consumption measured via RAPL on the host. We demonstrate, for the first time, guest-only resource-based energy estimation without privileged host access with experiments across diverse workloads, achieving high predictive accuracy and variance explained ($0.90 \leq R^2 \leq 0.97$), indicating the feasibility of guest-side energy estimation. This approach can enable energy-aware scheduling, cost optimization and physical host independent energy estimates in virtualized environments. Our approach addresses a critical gap in virtualized environments (e.g. cloud) where direct energy measurement is infeasible.

Energy landscapes play a crucial role in shaping dynamics of many real-world complex systems. System evolution is often modeled as particles moving on a landscape under the combined effect of energy-driven drift and noise-induced diffusion, where the energy governs the long-term motion of the particles. Estimating the energy landscape of a system has been a longstanding interdisciplinary challenge, hindered by the high operational costs or the difficulty of obtaining supervisory signals. Therefore, the question of how to infer the energy landscape in the absence of true energy values is critical. In this paper, we propose a physics-informed self-supervised learning method to learn the energy landscape from the evolution trajectories of the system. Experimental results across interdisciplinary systems demonstrate that our estimated energy has a correlation coefficient above 0.9 with the ground truth, and evolution prediction accuracy exceeds the baseline by an average of 17.65%. Energy landscapes are inherent in many stochastic dynamical systems in nature, such as the potential energy surface of protein conformations (Norn et al., 2021), the fitness landscape of species evolution (Papkou et al., 2023; Poelwijk et al., 2007), and the fractal energy landscapes of soft glassy materials. The evolution of these systems can be modeled as particles moving on the landscape under the combined effect of energy-driven drift and noise-induced diffusion. When multiple low-energy regions exist in the landscape, the combined effect of the energy gradient and noise induces high-frequency movement within individual regions and low-frequency transitions between different regions (Lin et al., 2024). In this context, energy landscapes have been applied to guide the generation of stable molecular structures (No e et al., 2019) and direct the evolution of proteins (Packer & Liu, 2015; Greenbury et al., 2022), and more recently, they have been incorporated as physical knowledge into deep learning for predicting system evolution (Guan et al., 2024; Wang et al., 2024b; Ding et al., 2024). Couce et al. (2024) cultivate 50,000 generations of bacteria to measure the fitness effects of mutations, while Sarkisyan et al. (2016) measure tens of thousands of luminescent protein genotypic sequences to construct the functional landscape. These manual experimental approaches are not only costly but also heavily reliant on expert knowledge. With the success of deep learning in numerous disciplines (Jumper et al., 2021; Han et al., 2023; Wang et al., 2023; Chen et al., 2024), several deep learning models have been proposed to estimate energy or equivalent quantities based on molecular spatial structures (Zhang et al., 2018), species genotypes (Tonner et al., 2022), or population compositions (Skwara et al., 2023). These methods still require high-cost annotations to provide supervisory signals for energy, which limits their practicality.

In this research, we propose a hybrid model for power plant detection to assist energy estimation applications, by pipelining GIS (Geographical Information Systems) having Remote Sensing capabilities with CNN (Convolutional Neural Networks) and ViT (Vision Transformers). Our proposed approach enables real-time analysis with multiple data types on a common map via the GIS, entails feature-extraction abilities due to the CNN, and captures long-range dependencies through the ViT. This hybrid approach is found to enhance classification, thus helping in the monitoring and operational management of power plants; hence assisting energy estimation and sustainable energy planning in the future. It exemplifies adequate deployment of machine learning methods in conjunction with domain-specific approaches to enhance performance.

Last-mile carriers increasingly incorporate electric vehicles (EVs) into their delivery fleet to achieve sustainability goals. This goal presents many challenges across multiple planning spaces including but not limited to how to plan EV routes. In this paper, we address the problem of predicting energy consumption of EVs for Last-Mile delivery routes using deep learning. We demonstrate the need to move away from thinking about range and we propose using energy as the basic unit of analysis. We share a range of deep learning solutions, beginning with a Feed Forward Neural Network (NN) and Recurrent Neural Network (RNN) and demonstrate significant accuracy improvements relative to pure physics-based and distance-based approaches. Finally, we present Route Energy Transformer (RET) a decoder-only Transformer model sized according to Chinchilla scaling laws. RET yields a +217 Basis Points (bps) improvement in Mean Absolute Percentage Error (MAPE) relative to the Feed Forward NN and a +105 bps improvement relative to the RNN.

An extensive air shower caused by a high-energy particle (cosmic or gamma ray) interacting with upper atmosphere can be detected by several methods including imaging atmospheric Cherenkov telescopes (IACTs). In Russian TAIGA (Tunka Advanced Instrument for cosmic ray physics and Gamma-ray Astronomy) experiment the number of installed and commissioned IACTs has been increased from one to two in 2020, and the third telescope was installed in 2020 [1]. Convolutional neural networks (CNNs) are a very successful machine learning tool. Several research teams have demonstrated high performance of CNNs for the analysis of images from IACTs and IACT arrays of several gamma astronomy experiments such as VERITAS [2], CTA [3], H.E.S.S. [4]. We previously applied CNNs to the analysis of images from a single TAIGA IACT, specifically, to the problems of identification of the event types and estimation of the energy of the original gamma rays [5, 6]. In this paper we apply convolutional neural networks to the identification of the event types and estimation of the energy of the original gamma rays based on images from one or two TAIGA Cherenkov telescopes and compare the neural network performance in monoscopic and stereoscopic modes.