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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…
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.…