To address the nonlinear amplification of numerical weather prediction (NWP) errors and the difficult trade-off between coverage and sharpness in short-term offshore wind power forecasting, this paper proposes PRWind, a physics-guided framework for s...
The integration of distributed photovoltaics (PV) introduces time-varying electrical coupling in active distribution networks, limiting the efficacy of conventional forecasting methods that rely on incomplete topological information and static physic...
Mechanical speed sensors increase cost and may reduce the reliability of permanent magnet synchronous motor (PMSM) drives under harsh conditions. This paper proposes a validation-protected physics-consistent probabilistic neural estimator for sensorl...
With the increasing penetration of renewable energy into power systems, wind and photovoltaic (PV) power generation exhibit substantial uncertainty due to meteorological variability, which becomes more pronounced under extreme weather conditions. The...
Peak annotation in Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a persistent bottleneck that typically requires the manual assignment of chemical formulas to hundreds of fragment ion peaks per spectrum. This work describes a physics-i...
Low-altitude aviation requires spatially resolved turbulence information, but routine observing networks do not directly measure dense three-dimensional eddy dissipation rate (EDR) fields. We formulate EDR retrieval as physics-constrained probabilist...
Deterministic and probabilistic models of measured quantities, processes, and fields in production process control systems, as well as physical and probabilistic measures, enable the formation of measurement results and confer them the properties of...
Micro-scale Cyber-Physical Systems (MCPSs) can be automatically and formally estimated by probabilistic model checking, on the level of system model MDPs (Markov Decision Processes) against desired requirements in PCTL (Probabilistic Computation Tree...
Unpredictability is an important concept throughout physics and plays a central role in quantum information theory. Despite this, little effort has been devoted to studying generalised notions or models of (un)predictability in physics. In this paper...
Land surface temperature (LST) is closely linked to land–atmosphere energy exchange, while conventional point forecasting cannot quantify forecast uncertainty or explicitly enforce physical consistency over long horizons. We propose Physics-for...
We show that the mutual information between two symbols, as a function of the number of symbols between the two, decays exponentially in any probabilistic regular grammar, but can decay like a power law for a context-free grammar. This result about f...
Every year, many countries carry out landslide susceptibility analyses to establish and manage countermeasures and reduce the damage caused by landslides. Because increases in the areas of landslides lead to new landslides, there is a growing need fo...
Deep-learning-based convection schemes have garnered significant attention for their notable improvements in simulating precipitation distribution and tropical convection in Earth system models. However, these schemes struggle to capture the stochast...
The shear strength (particularly soil cohesion) of rooted soil is an important parameter that reflects the true erodibility of meadows, particularly in meadows experiencing different degrees of degradation, ranging from undegraded (UD) through to lig...
Reliable probabilistic prediction of dam-break wave run-up is important for screening downstream barriers and river infrastructures. However, it remains unclear whether propagation conditions exhibit stable statistical groups within the observed pred...
Precise and efficient calculations are necessary to accurately assess the effects of thermal protection system (TPS) uncertainties on aerospacecrafts. This paper presents a probabilistic design methodology for TPSs based on physics-informed neural ne...
Electric vehicle (EV) charging has a large, spatially clustered, schedulable load whose vehicle-to-grid flexibility can be sold back to the power system. That flexibility has grid value only when the committed quantity can be reliably delivered under...
Wheel flats deteriorate normal wheel-rail contact conditions and induce periodic impacts, thereby affecting the operational stability and reliability of railway vehicles. In practical engineering applications, the number of fault samples available fo...
This study proposes a scalable Explainable Artificial Intelligence (XAI)–driven Bayesian–AI decision–control framework for multi-objective optimisation in uncertain and dynamic systems. The framework integrates Bayesian networks, st...
This paper proposes a physics-informed Bayesian framework for virtual testing and predictive modeling of train performance, specifically addressing stopping-distance prediction. The approach unifies physical simulation models with data-driven statist...
Short-term wind power forecasting depends on reliable Supervisory Control and Data Acquisition (SCADA) measurements and on models that can represent interactions across a turbine fleet. PG-RAGT is proposed for this setting as a physics-guided graph T...
Uncertainty-aware wind and photovoltaic (PV) forecasting must account for grid-related operating constraints. A conditional diffusion framework generates joint 24 h renewable scenarios using separate German EMHIRES series (262,968 hourly values), chr...
Predicting the Remaining Useful Life (RUL) of light aircraft landing gear is complicated by flight-to-flight variability in operational loads, particularly in sensor-free fleets that rely only on mass-and-balance records. This study develops a hybrid...
Renewable-generation forecasting is a probabilistic time-series problem in which point accuracy alone is insufficient for operational decision-making. This study proposes a physics-informed Bayesian framework for multi-horizon forecasting of solar, w...
Laboratory determination of uniaxial compressive strength (UCS) after in situ sampling is costly and slow. Coupling measurement while drilling (MWD) with machine learning can recover UCS in place. Most such models, however, return point estimates and...
A modern renewable energy forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being...
Photovoltaic (PV) forecasting in aerosol-affected, data-scarce environments is challenged by atmospheric heterogeneity, limited local measurements, and forecast uncertainty. This study develops a physics-guided artificial intelligence framework combi...
The seismic performance of tunnel structures is significantly influenced by the randomness of ground motions. Traditional probabilistic risk assessments, which rely on limited recorded ground-motion data, often suffer from small-sample bias and fail...
The accurate assessment of landslide hazards is important in order to reduce the casualties and damage caused by landslides. Landslide hazard assessment combines the evaluation of spatial and temporal probabilities. Although various statistical appro...
This thematic issue on advanced simulation tools applied to materials development and design predictions gathers selected extended papers related to power generation systems, presented at the XIX International Colloquium on Mechanical Fatigue of Meta...
The need to turn to more environmentally friendly sources of energy has led energy systems to focus on renewable sources of energy. Wind power has been a widely used source of green energy. However, the wind’s stochastic and unpredictable behav...
The results of the physical modelling of the formation processes of upward discharges from grounded objects in the artificial thunderstorm cell’s electric field are presented. We established the considerable influence of the electrode tip&rsquo...
In this paper, a high-security probabilistic constellation shaping transmission scheme based on recurrent neural networks (RNNs) is proposed, in which the constellation point probabilistic distribution is generated based on recurrent neural network t...
For robots in human environments, learning complex and demanding interaction skills from humans and responding quickly to human motions are highly desirable. A common challenge for interaction tasks is that the robot has to satisfy both the task spac...
Nowadays, our living environment has been embedded with smart objects, such as smart sensors, smart watches and smart phones. They make cyberspace and physical space integrated by their abundant abilities of sensing, communication and computation, fo...
Power grid dispatching is a high-risk process, and its execution depends on an available cyber system. However, the effects of cyber systems have not caught enough attention in current research on risk assessments in dispatching processes, which may...
SecuriDN v.
Modern power systems are facing growing complexity and uncertainty due to electrification, large-scale renewable integration, and evolving consumption behaviors. These changes have pushed traditional numerical optimization methods to their practical...
The physically based model has been widely used in rainfall-induced shallow landslide susceptibility analysis because of its capacity to reproduce the physical processes governing landslide occurrence and a higher predictive capability. However, one...
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference...
19 October 2022
This study presents an approach for the assessment of cyber-physical threats to water distribution networks under the prism of the uncertainty which stems from the variability and stochastic nature of nodal water demands. The proposed framework inves...
This paper extends and illustrates a probabilistic interpretation of the fractional derivative operator proposed by Pr. José Tenreiro Machado. While his interpretation concerned the probability of finding samples of the derivate signal in the...
Active area protection against unauthorized UAV swarms requires coordinated target assignment strategies that account for both low-level physical capabilities and the stochastic, consumptive nature of physical interceptions. This paper presents a dec...
The main aim of this study is probabilistic computer simulation of the effective physical parameters of fluids containing nanoparticles. A deterministic model following the rule of mixtures and some semi-empirical formulas are employed to calculate e...
(1) Background: A new probabilistic physico-chemical model of the drifting key parameter of measuring equipment is proposed. The model allows for the integrated consideration of degradation processes (electrolytic corrosion, oxidation, plastic accumu...
Ensembles of general circulation model (GCM) integrations yield predictions for meteorological conditions in future months. Such predictions have implicit uncertainty resulting from model structure, parameter uncertainty, and fundamental randomness i...
The main aim of this report is to inform the quantum information community about investigations on the problem of probabilistic compatibility of a family of random variables: a possibility to realize such a family on the basis of a single probability...
Traditional FRP durability forecasting predominantly treats performance evolution as a discrete “point-to-point” regression, inherently overlooking temporal coherence and stochastic uncertainty. This study proposes a novel probabilistic f...
Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. T...
Uncertainty is at the heart of decision-making processes in most real-world applications. Uncertainty can be broadly categorized into two types: aleatory and epistemic. Aleatory uncertainty describes the variability in the physical system where senso...
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