Strong shock waves generated during the pyrotechnic separation process of aerospace vehicles can cause high-frequency damage or even structural failure to the vehicle’s structure. Existing structural designs for shock attenuation typically rely...
Physics-informed DeepONet (PI_DeepONet) is utilized for the reconstruction task of structural displacement based on measured strain. For beam and plate structures, the PI_DeepONet is built by regularizing the strain–displacement relation and bo...
Traditional femtosecond laser modeling relies on the iterative solution of the Nonlinear Schrödinger Equation (NLSE) using the Split-Step Fourier Method (SSFM). However, SSFM’s high computational complexity leads to significant time consum...
This study presents a comparative investigation of Physics-Informed Neural Networks (PINNs) and data-driven Deep Operator Networks (DeepONets) for predicting the evolution of plane Newtonian Couette flow with dynamic wall slip. First, a PINN framewor...
This paper presents an integrated computational framework for predicting temperature fields in glulam beam–column connections under fire conditions, combining finite element modeling, automated parametric analysis, and deep learning techniques....
In the pursuit of accurate experimental and computational data while minimizing effort, there is a constant need for high-fidelity results. However, achieving such results often requires significant computational resources. To address this challenge,...
Evaluating failure probability for complex engineering systems is a computationally intensive task. While the Monte Carlo method is easy to implement, it converges slowly and, hence, requires numerous repeated simulations of a complex system to gener...
This study introduces the Graph-Structured Physics-Informed DeepONet (GS-PI-DeepONet), a novel neural network framework designed to address the challenges of solving parametric Partial Differential Equations (PDEs) in structural analysis, particularl...
Reliable multiscale models of thrombosis require platelet-scale fidelity at organ-scale cost, a gap that scientific machine learning has the potential to narrow. We trained a DeepONet surrogate on platelet dynamics generated with LAMMPS for platelets...
The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centrali...
With the deepening of unconventional oil and gas resource development, the gas–liquid two-phase flow phenomenon in horizontal gas wells is becoming increasingly complex. Accurate and efficient prediction of the flow state has become key to opti...
Financial time series prediction is a fundamental problem in investment and risk management. Deep learning models, such as multilayer perceptrons, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM), have been widely used in model...
Rapid three-dimensional electromagnetic field simulation in stratified marine environments is essential for underwater target sensing system design. This paper presents a Physics-Informed Deep Operator Network (PI-DeepONet) that integrates analytical...
Partial differential equations (PDEs) usually apply for modeling complex physical phenomena in the real world, and the corresponding solution is the key to interpreting these problems. Generally, traditional solving methods suffer from inefficiency a...
In oil and gas field development, accurate prediction of the near-wellbore stress distribution is important for ensuring wellbore integrity, particularly in tight shale reservoirs where the medium can be approximated as nearly homogeneous. The large...
Code duplication, commonly referred to as code cloning, is not inherent in software systems but arises due to various factors, such as time constraints in meeting project deadlines. These duplications, or “code clones”, complicate the pro...
Accurate traffic state estimation (TSE) is critical for the deployment of intelligent transportation systems. Physics-informed neural networks represent a significant approach for achieving TSE; however, their applicability is constrained by the avai...
Fourier Neural Operators (FNOs) exhibit mode saturation on high-contrast inhomogeneous media, and recent multi-scale extensions (MscaleFNO) further worsen out-of-distribution (OOD) generalization. We introduce the Helmholtz Neural Operator (HNO), a p...
The COVID-19 pandemic has led to a global health crisis with significant morbidity, mortality, and socioeconomic disruptions. Understanding and predicting the dynamics of COVID-19 are crucial for public health interventions, resource allocation, and...
Recent studies have demonstrated the potential of hyperbolic paraboloid (hypar), a doubly curved geometry, in coastal engineering applications. Predicting pressure distribution, critical for subsequent finite element analysis, on such novel three-dim...
Structural deformation of large-scale phased-array antennas under complex service environments seriously degrades the electromagnetic performance of radar systems. It is critical to achieve high-precision full-field deformation reconstruction of ante...
This review examines neural operator architectures for learning solution operators of parametric partial differential equations (PDEs), with an emphasis on conceptual clarity and practical implementation. The work analyzes key models, including DeepO...
Physics-Informed Neural Networks (PINNs) represent a transformative approach to solving partial differential equation (PDE)-based boundary value problems by embedding physical laws into the learning process, addressing challenges such as non-physical...
This study proposes a surrogate model, Graph-Structured Physics-Informed Deep Operator Network (GS-PI-DeepONet), to simulate two-dimensional hydrodynamics in a tidal river network. The model was coupled with Graph Convolutional Networks (GCNs) for sp...
Logistics operations demand real-time visibility and rapid response, yet minute-level traffic speed forecasting remains challenging due to heterogeneous data sources and frequent distribution shifts. This paper proposes a Deep Operator Network (DeepO...
Frictional finite-element solves of spur-gear tooth stress take tens of seconds. A ten-member Fourier neural operator (FNO) ensemble, trained on 270 of 432 quasi-static plane-strain solutions with Coulomb friction in the single-tooth contact zone, pr...
Optical microscopy is widely used across scientific, industrial, and clinical settings, yet its lateral resolution is fundamentally limited by diffraction (down to ~200 nm in 400 nm visible light). While super-resolution techniques can overcome this...
Wheel odometry is a simple and low-cost localization technique that can be used for localization in GNSS-deprived environments; however, its measurement accuracy is affected by many factors, such as wheel slip, wear, and tire pressure changes, result...
Digital technologies that continuously quantify animal behavior, physiology, and production offer significant potential for the early identification of health and welfare disorders of dairy cows. In this study, a multimodal artificial intelligence (A...
Predictive modeling of thermal aging in solid propellants is challenging because HTPB-based propellants are highly filled particle-reinforced polymer systems with sparse experimental data, nonlinear parameter coupling, and partially unclear aging mec...