This paper presents a comprehensive investigation into the applicability and performance of two prominent growth models, namely, the Verhulst model and the Montroll model, in the context of modeling tumor cell growth dynamics. Leveraging the power of...
This study investigates physics-informed and data-driven hybrid modeling strategies for an automotive-grade electrohydraulic (EH) semi-active damper system. Although deep sequence learning architectures such as Long Short-Term Memory (LSTM) networks...
Accurate impedance modeling of wind turbines (WTs) is essential for assessing the small-signal stability of power systems with high penetration of renewable energy. Existing approaches face a fundamental trade-off: physics-based “white-box&rdqu...
Flight dynamics modeling is a fundamental cornerstone of aircraft design, simulation, and control. Traditional approaches rely on aerodynamic look-up tables for numerical integration, which suffer from high data-acquisition costs, poor extrapolation...
Data-driven models for complex thermal systems face two main challenges: a heavy dependence on high-quality training datasets and a “black-box” nature that makes it difficult to align model predictions with fundamental physical laws. To a...
For rotor–bearing systems, their dynamic vibration models must be built to simulate the vibration responses that affect the safe and reliable operation of rotating machinery under different operating conditions. Single physics-based modeling me...
Urban building energy prediction is a critical challenge for sustainable city planning and large-scale retrofit prioritization. However, traditional data-driven models struggle to capture real urban environments’ spatial and morphological compl...
Physics-Informed Neural Networks (PINNs) have emerged as a powerful approach for integrating physical laws into a deep learning framework, offering enhanced capabilities for solving complex problems. Despite their potential, the practical implementat...
Transient conjugate heat transfer in coupled solid–fluid systems is governed by diffusion, advection, and heat transfer processes that can make repeated simulations expensive and time-consuming in the design phase. This study develops a physics...
Accurately modeling the system dynamics of autonomous underwater vehicles (AUVs) is imperative to facilitating the implementation of intelligent control. In this research, we introduce a physics-informed neural network (PINN) method to model the dyna...
Accurate forecasting of the lifetime and degradation mechanisms of lithium-ion batteries is crucial for their optimization, management, and safety while preventing latent failures. However, the typical state estimations are challenging due to complex...
Modeling thermal environments across scales is crucial for climate-adaptive design and energy management. Traditional numerical methods (e.g., CFD) offer high accuracy and physical consistency, but they are computationally expensive. In contrast, pur...
Reliable digital twins of complex nonlinear systems require not only high predictive accuracy but also physical consistency, robustness under degraded operating conditions, and explicit uncertainty handling. Purely data-driven models often suffer fro...
The application of digital twin (DT) technology to intelligent machining shows promise, but its effectiveness in predicting vibration and assessing surface quality has not been thoroughly validated for widespread industrial use. This study presents a...
This study develops a physics-informed surrogate for predicting three-dimensional gas–liquid hydrodynamics in an aerated double-impeller stirred tank. Reference fields were generated using steady-state computational fluid dynamics (CFD) simulat...
Physics-informed neural networks (PINN) can be used to predict flow fields with a minimum of simulated or measured training data. As most technical flows are turbulent, PINNs based on the Reynolds-averaged Navier–Stokes (RANS) equations incorpo...
A physics-informed neural network (PINN) framework is developed to model the large deformation and coupled electromechanical response of dielectric elastomer tubes for energy harvesting. The system integrates incompressible neo-Hookean elasticity wit...
Soil consolidation modeling is essential for estimating settlement and pore-water pressure dissipation, but analytical solutions are limited for layered soils with complex drainage and interface conditions. This study evaluates physics-informed neura...
Carburizing process optimization requires accurate prediction of multiple interrelated outcomes, yet existing models either oversimplify the physics or require prohibitively large datasets. Here, we present a physics-informed machine learning (PIML)...
This work has two objectives. Firstly, it describes a novel physics-informed hybrid neural network (PIHNN) model based on the long short-term memory (LSTM) neural network. The presented model structure combines the first-principle process description...
Accurate prediction of transient melt pool thermal fields in Laser Powder Bed Fusion (LPBF) is essential for understanding melt pool geometry and defect formation mechanisms, yet conventional finite element methods (FEM) impose prohibitive computatio...
Glucocorticoid-induced hypertension affects over 30% of treated patients, yet its underlying mechanisms remain unclear, particularly how glucocorticoids regulate renin within the renin-angiotensin-aldosterone system (RAAS). Modeling these dynamics is...
Therapeutic ultrasound waves are the main instruments used in many noninvasive clinical procedures. They are continuously transforming medical treatments through mechanical and thermal effects. To allow for effective and safe delivery of ultrasound w...
Graph neural networks are increasingly used for river water-quality forecasting because they can represent interactions among monitoring locations that conventional site-wise time-series models cannot capture. However, the usefulness of graph learnin...
Blast-induced ground vibration is traditionally predicted using empirical scaled-distance equations or numerical simulations driven by simplified analytical loading functions. Although dynamic finite element modeling has significantly advanced the un...
This paper presents a dynamic model for full-power converter permanent magnet synchronous wind turbines based on Physics-Informed Neural Networks (PINNs). The model integrates the physical dynamics of the wind turbine directly into the loss function,...
Efficient forward and inverse design of laminated non-pneumatic tires requires repeated evaluation of their load-bearing and tire–ground contact responses. The high-fidelity laminated beam–grounding analysis (LB-GA) formulation captures t...
With the rapid development of semiconductor technology, traditional equation-based modeling faces challenges in accuracy and development time. To overcome these limitations, neural network (NN)-based modeling methods have been proposed. However, the...
The conductor temperature of cross-linked polyethylene (XLPE) high-voltage direct-current (HVDC) cables governs ampacity assessment and insulation life management, yet it cannot be measured in service, and finite-element simulation is too expensive f...
This paper presents a physics-informed training framework for projection-based Reduced-Order Models (ROMs). We extend the original PROM-ANN architecture by complementing snapshot-based training with a FEM-based, discrete physics-informed residual los...
Recently, there has been a dramatic rise in the demand for accurate temperature forecasts. However, challenges arise from modeling and fusing complex spatial and temporal features in temperature data. In this study, we propose a physics-informed dire...
This study develops and analyzes a four-state nonlinear policy–feedback dynamical system that couples a system stressor, an accumulated burden, a signed mitigation–response variable, and a signed policy-pressure variable. The proposed mod...
Accurate modeling of bi-flux anomalous diffusion presents significant computational challenges in engineering. This paper investigates the effectiveness of physics-informed neural networks as surrogate models for the bi-flux anomalous diffusion equat...
The propagation of uncertainties in structural dynamic responses, arising from variations in material properties, geometry, and boundary conditions, is of critical concern to researchers in a variety of engineering instances. Conventional methods lik...
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&...
The rapid integration of renewable energy sources and increasing complexity of modern power grids demand advanced forecasting tools capable of simultaneously predicting multiple interconnected variables. While time series foundation models (TSFMs) ha...
Physics-informed neural networks (PINNs) have emerged as a promising approach for simulating nonlinear physical systems, particularly in the field of fluid dynamics and turbulence modelling. Traditional turbulence models often rely on simplifying ass...
Mechanistic tumor growth models are widely used to describe disease progression and treatment response, but their utility depends on accurate estimation of parameters governing the underlying biological processes. In this study, we employ a Physics-I...
Time series models are widely used to examine temporal dynamics and uncover patterns across diverse fields. A commonly employed approach for modeling such data is the (Vector) Autoregressive (AR/VAR) model, in which each variable is represented as a...
Drying is a complex process of simultaneous heat, mass, and momentum transport phenomena with continuous phase changes. Numerical modelling is one of the most effective tools to mechanistically express the different physics of drying processes for ac...
Models of chemical kinetic processes, comprising systems of stiff ordinary differential equations (ODEs), are essential for modeling important chemical reactions relevant to drinking water chemistry, such as disinfectant decay and disinfection byprod...
In this work, we apply a novel and accurate Physics-Informed Neural Network Theory of Functional Connections (PINN-TFC) based framework, called Extreme Theory of Functional Connections (X-TFC), for data-physics-driven parameters’ discovery of problem...
Air-operated double-diaphragm (AODD) pumps in industrial sludge transfer suffer from gradual performance degradation due to rheological variations and component wear, yet conventional monitoring relies on scarce labeled fault data. This paper present...
With the increase in the number of power electronic devices in power systems, various techniques for assessing their stability have emerged. Among these techniques, impedance model-based stability analysis techniques have been widely used. However, c...
Modelling and predicting the behaviour of infectious diseases is essential for early warning and evaluating the most effective interventions to prevent significant harm. Compartmental models produce a system of ordinary differential equations (ODEs)...
In recent years, hybrid systems combining data-driven and physics-based approaches have gained increasing attention for solving complex real-world problems where deterministic modeling alone is insufficient. Within this framework, Physics-Informed Ne...
With the rapid technological advancements in wearable devices, motion and health management have significantly improved, enabling the measurement of various biometric data with compact equipment. Our research focuses on motion measurement but, in gen...
This paper proposes a nonlinear Model Predictive Control (MPC) method based on Physics-Informed Neural Networks (PINNs), aimed at enhancing the trajectory tracking performance of Automated Guided Vehicles (AGVs) in complex dynamic environments. Tradi...
This study investigates a neural network model regularized by the Maxwell–Eucken formulation for predicting the thermal conductivity of foamed concrete. Backed by experimental measurements, the modeling workflow incorporates augmented samples d...
In silico studies of microbiological systems are essential for predicting and controlling the impact of external factors on bacterial communities. Quorum sensing represents one of the key mechanisms of bacterial communication, particularly in pathoge...
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