1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Robust identifiability for symbolic recovery of differential equations
Hillary Hauger, Philipp Scholl, Gitta Kutyniok
Recent advancements in machine learning have transformed the discovery of physical laws, moving from manual derivation to data-driven methods that simultaneously learn both the str…
cs.AI2023★ 1 cited
ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization
Philipp Scholl, Katharina Bieker, Hillary Hauger +1
The problem of symbolic regression (SR) arises in many different applications, such as identifying physical laws or deriving mathematical equations describing the behavior of finan…