3 papers
cs.LG2026
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…
physics.comp-ph2026
PENCO: A Physics-Energy-Numerics-Consistent Operator for 3D Phase Field Modeling
Mostafa Bamdad, Mohammad Sadegh Eshaghi, Cosmin Anitescu +2
Accurate and efficient solutions of spatiotemporal partial differential equations (PDEs), such as phase-field models, are fundamental for understanding interfacial dynamics and mic…
cs.LG2024
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.…