3 papers
cs.LG2026
Smoothness Errors in Dynamics Models and How to Avoid Them
Edward Berman, Luisa Li, Jung Yeon Park +1
Modern neural networks have shown promise for solving partial differential equations over surfaces, often by discretizing the surface as a mesh and learning with a mesh-aware graph…
cs.LG2026
On Uncertainty Calibration for Equivariant Functions
Edward Berman, Jacob Ginesin, Marco Pacini +1
Data-sparse settings such as robotic manipulation, molecular physics, and galaxy morphology classification are some of the hardest domains for deep learning. For these problems, eq…
cs.LG2024
The State of Julia for Scientific Machine Learning
Edward Berman, Jacob Ginesin
Julia has been heralded as a potential successor to Python for scientific machine learning and numerical computing, boasting ergonomic and performance improvements. Since Julia's i…