47 citations · 102 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…