← Papers

Neural ODEs

Neural Ordinary Differential Equations

Paper title Neural Ordinary Differential Equations

Chen et al. (University of Toronto, Vector Institute) · General physics · Public: code

Paper arXiv 1806.07366PDF

A neural network gives the time derivative of a hidden state, and a black-box ODE solver computes the output. Gradients come from the adjoint sensitivity method, a second ODE solved backwards in time, so training does not store the solver's intermediate steps.

Published
NeurIPS

Share on LinkedIn, X or Bluesky, or or .

Know related work that belongs here?

Something wrong on this page?

More papers

All papers
  1. PhysicsNeMo-Mesh — GPU-Native Meshes and Simulation Cases for Physics AI
  2. Multi-fidelity scaling — Compute-optimal data scaling via multi-fidelity training
  3. CAE-UQ — Predictive Uncertainty for Neural CAE Surrogates