197 citations · 229 across the 22 of their papers we have counts for
10 papers · 1 filter
An extended deep energy method for thermo-mechanical crack propagation
Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh +4
Thermo-mechanical fracture couples transient heat conduction on a cracked domain with a crack that grows as the temperature and the displacement evolve. Neural energy solvers have…
KernelOnet: An Interpretable Neural Operator Based on Kernel Functions
Yuan Guo, Hanshu Chen, Qiang Xi +2
This paper proposes an interpretable neural operator framework, the Kernel Operator Network (KernelOnet), which incorporates kernel functions explicitly into the neural operator ar…
A mesh-free multiresolution deep energy method with phase-field modeling of brittle fracture
Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh +4
Phase-field modeling of brittle fracture removes the need to track cracks explicitly by recasting their evolution as the minimization of an energy functional. In return it requires…
Neural Operators for Immersed-Boundary Soft Swimmers Locomotion
Mohammad Sadegh Eshaghi, Yizheng Wang, Navid Valizadeh +2
High-fidelity immersed-boundary simulation resolves the coupled motion of a deforming swimmer and its surrounding flow, but the resulting cost limits repeated evaluations for engin…
HAMNO: A Hierarchical Adaptive Multi-scale Neural Operator with Physics-Informed Learning for Dynamical Systems
Mostafa Bamdad, Mohammad Sadegh Eshaghi, Timon Rabczuk
Neural operators provide a powerful framework for learning solution mappings of partial differential equations directly in function space. However, many existing architectures stil…
Replay-Based Continual Learning for Physics-Informed Neural Operators
Yizheng Wang, Mohammad Sadegh Eshaghi, Xiaoying Zhuang +2
Neural operators generally demonstrate strong predictive performance on in-distribution (ID) problems. However, a critical limitation of existing methods is their significant perfo…
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…
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…
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…