2.1k citations · 9.4k across the 78 of their papers we have counts for
84 papers
Constitutive-Set Mechanics: variational mechanics on an admissible set of constitutive laws
Altaf Ahmad Lone, R. T. Durai Prabhakaran, Tawqeer Nasir Tak +2
A structural simulation needs a constitutive law, and experiments rarely determine one uniquely: several laws may fit the same data and satisfy the same physical constraints, yet d…
Mechanism-resolved phase-field fracture of composite shells with a certified admissible constitutive operator
Altaf Ahmad Lone, R. T. Durai Prabhakaran, Timon Rabczuk +1
Phase-field models of fracture in fibre-reinforced shells are usually formulated against a single closed-form stored energy, so that the stress, consistent tangent and plane-stress…
The role of weak interfaces in the tensile deformation and fracture of particle-filled polymers studied by phase-field model
Xu Chen, Ya Duan, Xiaoying Zhuang +1
Weak particle-matrix interfaces play a critical role in the tensile fracture of particle-filled polymer composites, but how they govern progressive debonding, fracture localization…
GA-VINO: A Geometry-Aware Variational Physics-informed Neural Operator for Mindlin-Reissner Plates
Siqi Wang, Daobo Sun, Yizheng Wang +4
Plate and shell structures are widely used in engineering fields. Rapid response prediction for such structures under complex geometries, heterogeneous materials, and varying loads…
Deep Energy Method with Large Language Model assistance: an open-source Streamlit-based platform for solving variational PDEs
Yizheng Wang, Cosmin Anitescu, Mohammad Sadegh Eshaghi +3
Physics-informed neural networks (PINNs) in energy form, also known as the deep energy method (DEM), offer advantages over strong-form PINNs such as lower-order derivatives and few…
Pretrain Finite Element Method: A Pretraining and Warm-start Framework for PDEs via Physics-Informed Neural Operators
Yizheng Wang, Zhongkai Hao, Mohammad Sadegh Eshaghi +4
We propose a Pretrained Finite Element Method (PFEM),a physics driven framework that bridges the efficiency of neural operator learning with the accuracy and robustness of classica…
Phase-field modeling of multicomponent vesicles in viscoelastic fluid
Zuowei Wen, Navid Valizadeh, Timon Rabczuk +1
Multicomponent vesicles suspended in viscoelastic fluids are crucial for understanding a variety of physiological processes. In this work, we develop a continuum surface force (CSF…
A coupled finite element-virtual element method for thermomechanical analysis of electronic packaging structures
Yanpeng Gong, Sishuai Li, Yue Mei +4
This study presents a finite element and virtual element (FE-VE) coupled method for thermomechanical analysis in electronic packaging structures. The approach partitions computatio…
Towards Unified AI-Driven Fracture Mechanics: The Extended Deep Energy Method (XDEM)
Yizheng Wang, Yuzhou Lin, Somdatta Goswami +8
Physics-Informed Neural Networks (PINNs) have recently emerged as powerful tools for solving partial differential equations (PDEs), with the Deep Energy Method (DEM) proving especi…
NOWS: Neural Operator Warm Starts for Accelerating Iterative Solvers
Mohammad Sadegh Eshaghi, Cosmin Anitescu, Navid Valizadeh +3
Partial differential equations (PDEs) underpin quantitative descriptions across the physical sciences and engineering, yet high-fidelity simulation remains a major computational bo…
Physics-Informed Kolmogorov-Arnold Networks for multi-material elasticity problems in electronic packaging
Yanpeng Gong, Yida He, Yue Mei +3
This paper proposes a Physics-Informed Kolmogorov-Arnold Network for analyzing elasticity problems in multi-material electronic packaging structures. The method replaces traditiona…
Multi-Head Neural Operator for Modelling Interfacial Dynamics
Mohammad Sadegh Eshaghi, Navid Valizadeh, Cosmin Anitescu +3
Interfacial dynamics underlie a wide range of phenomena, including phase transitions, microstructure coarsening, pattern formation, and thin-film growth, and are typically describe…
Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation
Yizheng Wang, Jinshuai Bai, Mohammad Sadegh Eshaghi +4
AI for PDEs has garnered significant attention, particularly Physics-Informed Neural Networks (PINNs). However, PINNs are typically limited to solving specific problems, and any ch…
A first-principles study on the physical properties of two-dimensional Nb3Cl8, Nb3Br8 and Nb3I8
Bohayra Mortazavi, Xiaoying Zhuang, Timon Rabczuk
In a recent advance, Nb3Cl8 two-dimensional crystals with a kagome lattice and electronic topological flat bands has been experimentally fabricated (Nano Lett. 2022, 22, 4596). In…
Variational Physics-informed Neural Operator (VINO) for Solving Partial Differential Equations
Mohammad Sadegh Eshaghi, Cosmin Anitescu, Manish Thombre +3
Solving partial differential equations (PDEs) is a required step in the simulation of natural and engineering systems. The associated computational costs significantly increase whe…
Artificial intelligence for partial differential equations in computational mechanics: A review
Yizheng Wang, Jinshuai Bai, Zhongya Lin +9
In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Scien…
Applications of Scientific Machine Learning for the Analysis of Functionally Graded Porous Beams
Mohammad Sadegh Eshaghi, Mostafa Bamdad, Cosmin Anitescu +3
This study investigates different Scientific Machine Learning (SciML) approaches for the analysis of functionally graded (FG) porous beams and compares them under a new framework.…
Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
Yizheng Wang, Jia Sun, Jinshuai Bai +5
AI for partial differential equations (PDEs) has garnered significant attention, particularly with the emergence of Physics-informed neural networks (PINNs). The recent advent of K…
Boosting output performance of contact-separation mode triboelectric nanogenerators by adopting discontinuity and fringing effect: experiment and modelling studies
Teresa Cheng, Han Hu, Navid Valizadeh +6
Triboelectric nanogenerators (TENGs) are promising self-powering supplies for a diverse range of intelligent sensing and monitoring devices, especially due to their capability of h…
Phase field method for quasi-static hydro-fracture in porous media under stress boundary condition considering the effect of initial stress field
Shuwei Zhou, Xiaoying Zhuang, Timon Rabczuk
Phase field model (PFM) is an efficient fracture modeling method and has high potential for hydraulic fracturing (HF). However, the current PFMs in HF do not consider well the effe…
Polytopal composite finite elements for modeling concrete fracture based on nonlocal damage models
Hai D. Huynh, S. Natarajan, H. Nguyen-Xuan +1
The paper presents an assumed strain formulation over polygonal meshes to accurately evaluate the strain fields in nonlocal damage models. An assume strained technique based on the…
Phase field modeling of hydraulic fracture propagation in transversely isotropic poroelastic media
Shuwei Zhou, Xiaoying Zhuang
This paper proposes a phase field model (PFM) for describing hydraulic fracture propagation in transversely isotopic media. The coupling between the fluid flow and displacement fie…
Phase Field Characterization of Rock Fractures in Brazilian Splitting Test Specimens Containing Voids and Inclusions
Shuwei Zhou, Xiaoying Zhuang, Jiaming Zhou +1
The Brazilian splitting test is a widely used testing procedure for characterizing the tensile strength of natural rock or rock-like material due to the fact. However, the results…
Phase-field modeling of fluid-driven dynamic cracking in porous media
Shuwei Zhou, Xiaoying Zhuang, Timon Rabczuk
A phase field model for fluid-driven dynamic crack propagation in poroelastic media is proposed. Therefore, classical Biot poroelasticity theory is applied in the porous medium whi…