2 citations · 2 across the 3 of their papers we have counts for
3 papers
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…
cs.CL2024
REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
Nacime Bouziani, Shubhi Tyagi, Joseph Fisher +2
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). Howe…
cs.LG2023★ 2 cited
Physics-driven machine learning models coupling PyTorch and Firedrake
Nacime Bouziani, David A. Ham
Partial differential equations (PDEs) are central to describing and modelling complex physical systems that arise in many disciplines across science and engineering. However, in ma…