Frost heave in seasonally frozen loess regions is fundamentally governed by pore water migration towards the freezing front driven by temperature gradients, forming ice lenses that damage engineered infrastructure. Because both freezing intensity and...
Aircraft trajectory prediction (ATP) is a critical technology for air traffic control (ATC), safeguarding aviation safety and airspace resource management. To address the limitations of existing methods—kinetic models’ susceptibility to e...
This study investigates how incorporating physical constraints can enhance the performance of machine learning models by ensuring that geotechnical drilling data predictions align with known physical conditions at the site. Machine learning-predicted...
High-volume, long-duration flowback in ultra-deep fractured wells couples pressure, rate, water production, fracture conductivity, and stress-sensitive reservoir properties, making it difficult for pressure transient analysis (PTA), flowing material...
The efficient development of low-permeability reservoirs is critically constrained by severe geological heterogeneity, marginal permeability (<10 mD), and the consequent prevalence of low-productivity wells. Conventional intermittent production ma...
The continuous prediction and uncertainty quantification of unconfined compressive strength (UCS) and the fracture-related index of anisotropy (FRIA) are essential for optimizing drilling operations and hydraulic fracturing design in shale gas develo...
Flood susceptibility mapping in high-altitude ungauged basins faces a structural dichotomy: physically based models often suffer from systematic biases due to uncertain satellite precipitation, whereas data-driven models are prone to overfitting and...
Machine-learning surrogates predict composite properties but report no calibrated reliability and may violate physics. Bound-constrained conformal prediction confines intervals to physical bounds but assumes them exact. This paper keeps the corridor...
The selectivity of a molecularly imprinted polymer (MIP) is decided the moment its functional monomer is chosen, yet that choice is still made largely by trial and error and without any statement of confidence, so a confident but wrong pick can commi...
Machine-learning performance estimates for densely sampled thermogravimetric analysis (TGA) data depend on whether individual observations or complete thermograms are withheld. Six regression models were evaluated on seven thermograms from untreated,...
In summary, near-surface icing risk in complex alpine terrain is jointly controlled by freezing conditions, moisture supply, freeze–thaw transitions, and topographic energy processes. Traditional approaches relying on sparse station data or sin...
Bond wire degradation represents the predominant failure mechanism in IGBT modules, accounting for approximately 70% of power converter failures and posing significant reliability challenges in modern power electronic systems. Existing monitoring tec...
Quantitative precipitation estimation (QPE) from radar reflectivity is fundamental for weather nowcasting and water resource management. Conventional Z-R relationship formulas, derived from Rayleigh scattering theory, rely heavily on empirical parame...
Accurately quantifying vertical sediment transport rates in large seaward rivers is vital for estimating basin-scale water and sediment fluxes and assessing riverbed evolution. Traditional multi-point velocity and suspended sediment concentration (SS...
Single-sided formwork supporting systems (SFSSs) play a crucial role in the urban construction of retaining walls using cast-in-place concrete. By supporting the formwork from one side, an SFSS can minimize its spatial footprint, enabling its closer...
Image-based structural damage assessment requires automated methods that respect physical constraints. We formulate graph-neural-network message passing as a pseudo-time iteration of a monotone operator on a spatial-region graph. Under non-negative p...
This study proposes an approach for predicting the state of charge (SoC) of an electric vehicle battery at the end of an individual operational interval using parameters available before the start of the trip. The input features included the initial...
This study aimed to develop and validate a physics-informed neural network (PINN) framework for data-efficient and physically consistent process optimization in the laser powder bed fusion (LPBF) of Inconel 718 (IN718) superalloy. Laser powder bed fu...
Coal gangue backfill serves as a primary supporting structure for overlying strata in mined-out areas, and its internal damage evolution is directly associated with the safety and stability of mining operations. Acoustic emission (AE) technology prov...
Compression index (Cc) and recompression index (Cur) are essential parameters in one-dimensional consolidation and settlement analysis, yet their direct determination from oedometer testing is time-consuming, costly, and often limited by sparse recom...
Accurate photovoltaic (PV) power prediction under limited experimental data remains a significant challenge, particularly when purely data-driven models generate predictions that violate fundamental physical constraints. This study proposes a physics...
Organic solar cells (OSCs) have emerged as a competitive alternative to conventional silicon-based photovoltaics for their inexpensive production, versatility, and reduced energy consumption. However, it is still challenging to accurately assess thei...
Machine learning (ML) has emerged as a transformative tool in geotechnical engineering, offering data-driven alternatives to traditional empirical and analytical approaches. This paper presents a critical narrative review of recent advances in theory...
Extracting Cole–Cole relaxation parameters from broadband dielectric spectroscopy (BDS) of cross-linked polyethylene (XLPE) cable insulation is ill-conditioned when the dispersion peak lies outside the instrument frequency window. This paper pr...
Purely data-driven machine-learning methods are currently limited by weak physical interpretability; meanwhile, the sparsity of well-site data in oil and gas fields further degrades the prediction performance of deep learning models for reservoir see...
Electrical Discharge Machining (EDM) is widely used for precision machining of intricate parts in many industrial applications. The process output and machining performance of the EDM process are highly affected by the complexity of the EDM process a...
Subsurface thermo-hydro-mechanical (THM) coupled processes are fundamental to geomechanics, yet conventional mesh-based methods face high computational costs and limited efficiency in strongly nonlinear simulations and inverse problems. This review e...
Modern automotive steering systems exhibit inherent bilateral symmetry characteristics that can be mathematically described using group theory. When component failures occur, these systems experience quantifiable symmetry-breaking patterns that serve...
Non-recurrent congestion (NRC), caused by unforeseen events such as crashes, lane closures, and adverse weather, accounts for approximately half of all delays on urban freeways, yet it remains difficult to distinguish from routine congestion at recur...
Sea surface salinity (SSS) observations play a crucial role in the study of ocean circulation, climate variability, and marine ecosystems. However, current satellite SSS products suffer from systematic biases due to factors such as radio frequency in...
This review surveys recent progress in hybrid artificial intelligence (AI) approaches for gas turbine intelligent digital twins, with an emphasis on models that integrate physics-based simulations and machine learning. The main contribution is the in...
Polymeric drag-reducing agents (DRAs) can significantly suppress turbulent friction in crude oil pipelines. However, the design of field dosing strategies still relies on empirical rules because laboratory-scale drag-reduction (DR) measurements fail...
The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially larg...
Accurate reservoir outflow simulation is crucial for water resource management. However, traditional machine learning-based simulation methods have not sufficiently considered the physical constraints of reservoir operation, which may lead to unreali...
Dripping faucets exhibit chaotic dynamics with order–chaos transitions and multi-field coupling. This study built a physics-informed neural network (PINN) model using high-speed camera and numerical simulation data. The model incorporates physi...
Five-axis CNC machining involves strong coupling among machining quality, material removal efficiency, and operational safety, making it difficult to obtain adaptive and feasible process parameters using conventional scalar-objective optimization met...
With the acceleration of global urbanization and the large-scale development of underground spaces, underground engineering faces extremely complex and variable geological environments and high-risk construction disturbances. The widespread applicati...
This study utilizes machine-learning algorithms to reinterpret existing datasets originally plotted using Decline-Curve Analysis (DCA), aiming to enhance predictive accuracy without requiring new field-data acquisition. Historical production records...
As global energy systems pursue lower-carbon transition pathways, the rapid expansion of shale-gas development has increased subsurface fluid injection, intensifying concerns about induced seismicity and associated seismic risk. Although regional stu...
Surrogate modelling is increasingly used in engineering to improve computational efficiency in complex simulations. However, traditional data-driven surrogate models often face limitations in generalizability, physical consistency, and extrapolation&...
Two-dimensional transition metal carbides and nitrides, known collectively as MXenes, are attractive photocatalyst candidates because their surface chemistry and atomic composition can be tuned over a wide compositional window. A crucial design quant...
In solid mechanics, data-driven approaches are widely considered as the new paradigm that can overcome the classic problems of constitutive models such as limiting hypothesis, complexity, and accuracy. However, the implementation of machine-learned a...
In this study, a method was introduced to validate the presence of a Representative Elementary Volume (REV) within marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure. Physical testing provided the bas...
Predicting the terminal settling velocity of particles in non-Newtonian fluids remains a persistent challenge in multiphase flow modeling, with applications spanning chemical processing, mineral processing, and geophysical fluid dynamics. Traditional...
Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile device...
Metal additive manufacturing (MAM) enables the production of complex, high-value components for sectors such as aerospace, energy, and biomedical engineering. However, its large-scale industrial adoption remains constrained by internal defects, resid...
Atmospheric precipitable water vapor (PWV) is an essential geophysical parameter for weather forecasting, climate change research and hydrological studies. Spaceborne passive microwave sensors support all-weather PWV observation, yet accurate retriev...
Surface water quality is critical to ecosystem health and sustainable development, yet conventional monitoring falls short of spatiotemporally continuous assessment. Remote sensing coupled with machine learning has become a powerful paradigm for larg...
Gravity inversion can be used to obtain the spatial structure and physical properties of subsurface anomalies through gravity observation data. With the continuous development of machine learning, geophysical inversion methods based on deep learning...
Manufacturing-as-a-service (MaaS) platforms require fast and accurate manufacturing cost estimation to support quotation and resource allocation across distributed networks. However, existing machine learning approaches are often constrained by limit...
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