75 citations · 156 across the 16 of their papers we have counts for
18 papers
BRIDGE: Bridging Routine Inputs and Digital modelling for Geotechnical Engineering
Roshan Philip Saji, Waleed El-Sekelly, Tarek Abdoun +1
The mechanical properties needed for geotechnical design require laboratory tests and are thus available for far fewer field samples than routinely measured basic (index) propertie…
Physics-Audited Agentic Discovery in Scientific Machine Learning
Diab W. Abueidda, Bilal Ahmed, Panos Pantidis +1
In agentic scientific machine learning (SciML), large language model (LLM) agents can discover surrogate models and select one by an automated score, typically an error metric. A l…
A hybrid IFENN solver for generalizable modeling of phase-field fracture initiation and propagation
Panos Pantidis, Fouad Amin, Diab Abueidda +1
In this paper we demonstrate how the Integrated Finite Element Neural Network (IFENN) framework can effectively model the entire evolution of phase-field fracture, including the in…
Adaptive Distance-Aware Trunk Deep Operator Learning for Long-Span Roadway Bridges
Bilal Ahmed, Diab W. Abueidda, Waleed El-Sekelly +2
Long-span roadway bridges exhibit highly localized structural responses under vehicular loading, making repeated FE analysis computationally expensive for applications such as infl…
I-FENN with DeepONets: accelerating simulations in coupled multiphysics problems
Fouad M. Amin, Diab W. Abueidda, Panos Pantidis +1
Coupled multiphysics simulations for high-dimensional, large-scale problems can be prohibitively expensive due to their computational demands. This article presents a novel framewo…
Time Resolution Independent Operator Learning
Diab W. Abueidda, Mbebo Nonna, Panos Pantidis +1
Accurately learning solution operators for time-dependent partial differential equations (PDEs) from sparse and irregular data remains a challenging task. Recurrent DeepONet extens…
Modified non-local damage model: resolving spurious damage evolution
Roshan Philip Saji, Panos Pantidis, Mostafa E. Mobasher
Accurate prediction of damage and fracture evolution is critical for the safety design and preventive maintenance of engineering structures, however existing computational methods…
Integrated Finite Element Neural Network (IFENN) for Phase-Field Fracture with Minimal Input and Generalized Geometry-Load Handling
Panos Pantidis, Lampros Svolos, Diab Abueidda +1
We present a novel formulation for modeling phase-field fracture propagation based on the Integrated Finite Element Neural Network (IFENN) framework. IFENN is a hybrid solver schem…
Physics-informed Multiple-Input Operators for efficient dynamic response prediction of structures
Bilal Ahmed, Yuqing Qiu, Diab W. Abueidda +3
Finite element (FE) modeling is essential for structural analysis but remains computationally intensive, especially under dynamic loading. While operator learning models have shown…
Image-based adaptive domain decomposition for continuum damage models
Panos Pantidis, Cornelius Otchere, Mostafa E. Mobasher
We present a novel image-based adaptive domain decomposition FEM framework to accelerate the solution of continuum damage mechanics problems. The key idea is to use image-processin…
Damage identification for bridges using machine learning: Development and application to KW51 bridge
Yuqing Qiu, Bilal Ahmed, Diab W. Abueidda +6
The available tools for damage identification in civil engineering structures are known to be computationally expensive and data-demanding. This paper proposes a comprehensive mach…
Physics-informed DeepONet with stiffness-based loss functions for structural response prediction
Bilal Ahmed, Yuqing Qiu, Diab W. Abueidda +4
Finite element modeling is a well-established tool for structural analysis, yet modeling complex structures often requires extensive pre-processing, significant analysis effort, an…
DeepOKAN: Deep Operator Network Based on Kolmogorov Arnold Networks for Mechanics Problems
Diab W. Abueidda, Panos Pantidis, Mostafa E. Mobasher
The modern digital engineering design often requires costly repeated simulations for different scenarios. The prediction capability of neural networks (NNs) makes them suitable sur…
I-FENN with Temporal Convolutional Networks: expediting the load-history analysis of non-local gradient damage propagation
Panos Pantidis, Habiba Eldababy, Diab Abueidda +1
In this paper, we demonstrate for the first time how the Integrated Finite Element Neural Network (I-FENN) framework, previously proposed by the authors, can efficiently simulate t…
A new unified arc-length method for damage mechanics problems
Roshan Philip Saji, Panos Pantidis, Mostafa E. Mobasher
The numerical solution of continuum damage mechanics (CDM) problems suffers from convergence-related challenges during the material softening stage, and consequently existing itera…
I-FENN for thermoelasticity based on physics-informed temporal convolutional network (PI-TCN)
Diab W. Abueidda, Mostafa E. Mobasher
Most currently available methods for modeling multiphysics, including thermoelasticity, using machine learning approaches, are focused on solving complete multiphysics problems usi…
Error convergence and engineering-guided hyperparameter search of PINNs: towards optimized I-FENN performance
Panos Pantidis, Habiba Eldababy, Christopher Miguel Tagle +1
In our recently proposed Integrated Finite Element Neural Network (I-FENN) framework (Pantidis and Mobasher, 2023) we showcased how PINNs can be deployed on a finite element-level…
Integrated Finite Element Neural Network (I-FENN) for non-local continuum damage mechanics
Panos Pantidis, Mostafa E. Mobasher
We present a new Integrated Finite Element Neural Network framework (I-FENN), with the objective to accelerate the numerical solution of nonlinear computational mechanics problems.…