447 citations · 803 across the 11 of their papers we have counts for
11 papers
Nature's Cost Function: Simulating Physics by Minimizing the Action
Tim Strang, Isabella Caruso, Sam Greydanus
In physics, there is a scalar function called the action which behaves like a cost function. When minimized, it yields the "path of least action" which represents the path a physic…
A Tutorial on Structural Optimization
Sam Greydanus
Structural optimization is a useful and interesting tool. Unfortunately, it can be hard for new researchers to get started on the topic because existing tutorials assume the reader…
Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately
Andrew Sosanya, Sam Greydanus
Understanding natural symmetries is key to making sense of our complex and ever-changing world. Recent work has shown that neural networks can learn such symmetries directly from d…
Piecewise-constant Neural ODEs
Sam Greydanus, Stefan Lee, Alan Fern
Neural networks are a popular tool for modeling sequential data but they generally do not treat time as a continuous variable. Neural ODEs represent an important exception: they pa…
The Story of Airplane Wings
Sam Greydanus
The purpose of this work is to explain how wings work and how they were invented. We use the lens of history, looking at the individual people who wanted to fly, the lens of techno…
Lagrangian Neural Networks
Miles Cranmer, Sam Greydanus, Stephan Hoyer +3
Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet…