3 papers
cs.CE2026
Missing Physics Discovery through Fully Differentiable Finite Element-Based Machine Learning
Ado Farsi, Nacime Bouziani, David A Ham
Modelling physical systems with partial differential equations (PDEs) is central to science and engineering, yet in most real applications the PDE model is incomplete: relationship…
cs.LG2024
Structure-Preserving Operator Learning
Nacime Bouziani, Nicolas Boullé
Learning complex dynamics driven by partial differential equations directly from data holds great promise for fast and accurate simulations of complex physical systems. In most cas…
cs.LG2024
Differentiable programming across the PDE and Machine Learning barrier
Nacime Bouziani, David A. Ham, Ado Farsi
The combination of machine learning and physical laws has shown immense potential for solving scientific problems driven by partial differential equations (PDEs) with the promise o…