This paper presents the RaDICAL sensing framework, a monostatic passive radar concept that combines a Sparse Uniform Circular Array (SUCA), deterministic multifrequency dither, and dictionary-based waveform recognition for target detection and classi...
Constructing accurate Potential Energy Surfaces (PES) is a central task in molecular modeling, as it determines the forces governing nuclear motion and enables reliable quantum dynamics simulations. While ab initio methods can provide accurate PES, t...
This study presents a comparative analysis of wave buoy analogy models for sea state estimation. A nonparametric, response amplitude operator-based model is introduced as a physics-based approach, while a convolutional neural network is adopted as a...
Coding metasurfaces provide an effective approach for manipulating electromagnetic scattering through discrete coding states. However, obtaining angular-domain scattering responses for a large number of coding configurations remains computationally e...
Differentiable rendering techniques have received significant attention from both industry and academia for novel view synthesis or for reconstructing shapes and materials from one or multiple input photographs. These techniques are used to propagate...
Structural damage identification under environmental and operational variations (EOVs) remains a major challenge in civil infrastructure health monitoring because environmental shifts can be statistically correlated with damage-sensitive vibration fe...
Rolling bearings are one of the core components of industrial equipment. Owing to the rapid development of deep learning methods, a multitude of data-driven remaining useful life (RUL) estimation approaches have been proposed recently. However, sever...
Groundwater is a crucial source of the world’s drinking and irrigation water. Nonetheless, it is being rapidly depleted in many parts of the world. To enact policy decisions to preserve this precious resource, policymakers need real-time data o...
Accurately predicting the spatiotemporal evolution of rock-damage zones is vital for underground engineering safety. Using three-dimensional data obtained from uniaxial compression–acoustic emission tests, this study addresses the key limitatio...
Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. However, traditional physics-based models lack adaptability, while machine learning approaches are often limited...
This study introduces a physics-guided self-supervised framework for few-shot ultrasonic defect detection in concrete structures, addressing the dual challenges of scarce labels and domain variability in structural health monitoring (SHM). Our method...
Early detection of battery degradation is essential for ensuring the safety and reliability of electric vehicle (EV) systems under real-world operating variability. This paper proposes a physics-guided multi-sensor learning framework, termed SensorFu...
Traditional routing algorithms optimizing for distance or travel time are inadequate for electric vehicles (EVs), which require energy-aware planning considering battery constraints and charging infrastructure. This work presents an energy-optimal ro...
The retrieval of attosecond and femtosecond pulses from attosecond streaking spectrograms is a high-dimensional ill-posed inverse problem. The traditional FROG CRAB (frequency-resolved optical gating for complete reconstruction of attosecond bursts)...
Health indicators (HIs) are quantitative indices that assess the condition of engineering systems by linking sensor data with monitoring, diagnostic, and prognostic methods to estimate the remaining useful life (RUL). Digital twins (DTs), which serve...
Ground-based astronomical images are often degraded by atmospheric turbulence and deterministic optical aberrations introduced by telescope design and manufacturing processes. Joint mitigation of these distortions remains challenging due to the lack...
Neutron/gamma Pulse Shape Discrimination (PSD) is a critical task in radiation detection systems, where reliable classification becomes increasingly challenging under low Signal-to-Noise Ratio (SNR) conditions. Although Deep Convolutional Neural Netw...
In-cylinder pressure provides critical insights for analyzing and optimizing combustion in internal combustion engines, yet its acquisition across the full operating space requires extensive testing, while physics-based models are computationally dem...
Learned methods for seismic travel-time tomography are usually compared with physics-based inversion on benchmarks whose representation and evaluation choices are left implicit. We show that the apparent workflow ordering depends materially on the de...
Bridging the gap between Artificial Intelligence (AI)-driven generative design and robotic fabrication remains a critical challenge in architectural automation. While Generative Artificial Intelligence (GenAI) tools have advanced conceptual design wo...
This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-e...
Component degradation in aero-engines can cause local model mismatch. Insufficient coordination between component-response updates and engine-level prediction correction may limit improvements in gas-path prediction accuracy. Therefore, a data-enhanc...
Sensor health assessments are of great importance for accurately understanding the health of an aeroengine, supporting maintenance decisions, and ensuring flight safety. This study proposes an intelligent framework based on a physically guided neural...
This study proposes a supervised machine learning framework for vibration-based fault diagnosis of rotating machinery using integrated data-driven and physics-informed feature sets. A dataset acquired under variable load and multiple operating condit...
Accurate modeling of deformable object dynamics is critical for robotic manipulation but remains challenging due to complex physics and strict physical constraints. In this paper, PhysCon-Deform is introduced, which is a mixed framework for specific...
To address the low contrast of X-ray weakly absorbing materials, phase retrieval (PR) is employed for contrast enhancement and material identification but inevitably leads to structural blurring and boundary smoothing due to its equivalent low-pass f...
Proteins are dynamic macromolecules whose functions are intricately linked to their structural flexibility. Recent breakthroughs in deep learning have enabled accurate prediction of static protein structures. However, understanding protein function i...
In many real-world settings, image observations of freely rotating 3D rigid bodies may be available when low-dimensional measurements are not. However, the high-dimensionality of image data precludes the use of classical estimation techniques to lear...
Accurate underwater acoustic localization is a key enabling capability for autonomous underwater vehicles and underwater drones, which cannot rely on satellite positioning while submerged and therefore depend on acoustic methods to determine their po...
Accurate power prediction is essential for assessing wind turbine performance under real-world operating conditions and for supporting condition monitoring and maintenance planning using SCADA data. Most existing approaches rely directly on raw SCADA...
Coupled fire-atmosphere models are increasingly being used to study low-intensity fires, such as those that are used in prescribed fire applications. Thus, the need arises to evaluate these models for their ability to accurately represent fire spread...
The objective of this paper is to develop a two-step predict and correct non-intrusive Parametric Model Order Reduction (PMOR) methodology for the problem of changing well locations in an oil field that can eventually be used for well placement optim...
As the core component of aircraft systems, aeroengines require accurate Remaining Useful Life (RUL) prediction to ensure flight safety, which serves as a key part of Prognostics and Health Management (PHM). Traditional RUL prediction methods primaril...
Achieving optimal daylighting is a cornerstone of sustainable architectural design, impacting energy efficiency and occupant well-being. Fast and accurate prediction during the conceptual phase is crucial but challenging. While physics-based simulati...
Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their practical implementation is constrained by the strongly nonlinear and multiphysics nature...
Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reaso...
In nuclear spectroscopy, a physical phenomenon known as the pile-up effect distorts direct measurements by causing temporal overlap of detector pulses. Existing deep learning-based pile-up correction methods rely heavily on supervised training with s...
Characterizing multiscale fracture networks in shale gas reservoirs remains challenging, while the limited applicability of conventional continuum-based models and insufficient multi-objective coordination often lead to low efficiency in development...
Fire spotting (FS), the process by which firebrands are lofted, transported, and ignite new fires ahead of the main flame front, plays a critical role in escalating extreme wildfire events. This systematic literature review (SLR) analyzes peer-review...
Accurate prediction of building energy consumption is critical for sustainable urbanization, while dynamic occupant interactions with the building envelope remain a major source of uncertainty. Prevailing cross-scale building energy models often rely...
Accurate ice-period river monitoring is essential for characterizing ice jam floods (IJFs) and associated jam-release waves (JAVEs). However, physics-based and machine-learning (ML) approaches remain constrained by data scarcity, uncertainty, and inc...
Designing linear conveyor feeders with passive fences for automated part orientation remains largely trial and error because the final orientation distribution is difficult to predict reliably before physical testing. We present a simulation-driven d...
The diagnosis and prognosis of aeronautical-bearing health conditions are essential to proactively ensuring efficient power transmission, safety, and reduced downtime. The rarity of failures in such safety-critical systems drives this process towards...
The increasing frequency and magnitude of landslides underscore the growing importance of landslide prediction in light of factors like climate change. Traditional methods, including physics-based methods and empirical methods, are beset by high cost...
Recent advances in transportation technologies highlight the demand for faster and more efficient systems. Magnetic levitation has emerged as a key enabler in high-speed concepts such as the Hyperloop by eliminating mechanical contact, reducing frict...
In the Mediterranean basin, wildfires burn an average of 600,000 ha per year, causing severe ecological, economic, and social impacts. Fire behavior modeling is essential for wildfire prevention and control. Three-dimensional physics-based fire behav...
Condition monitoring in engineering systems requires analytical frameworks that connect physically meaningful signal representations with statistically consistent decision mechanisms. Although spectral analysis, time–frequency methods, and mach...
Although the diagnosis and treatment of depression is a medical field, ICTs and AI technologies are used widely to detect depression earlier in the elderly. These technologies are used to identify behavioral changes in the physical world or sentiment...
As hydraulic buildings are constantly subjected to complex interactions with water, particularly variations in hydrostatic pressure and temperature, deformation structural behavior is inherently sensitive to environmental fluctuations. Monitoring dam...
Predictive maintenance (PdM) has proven to be a critical strategy for minimizing downtime and optimizing operational efficiency in industrial systems. In this work the authors propose a data-driven PdM framework for circulating water pumps (CWPs), co...
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