3 papers
cs.LG2026
Recovering Governing Equations from Solution Data: Identifiability Bounds for Linear and Nonlinear ODEs
Yang Pan, Helmut Bölcskei
Learning governing equations from observed solution data is a fundamental challenge in scientific machine learning, yet the theoretical conditions under which a ground-truth ODE ca…
cs.LG2026
Generating Rectifiable Measures through Neural Networks
Erwin Riegler, Alex Bühler, Yang Pan +1
We derive universal approximation results for the class of (countably) -rectifiable measures. Specifically, we prove that -rectifiable measures can be approximated as push-fo…
cs.LG2025
Metric-Entropy Limits on the Approximation of Nonlinear Dynamical Systems
Yang Pan, Clemens Hutter, Helmut Bölcskei
This paper is concerned with fundamental limits on the approximation of nonlinear dynamical systems. Specifically, we show that recurrent neural networks (RNNs) can approximate non…