2 citations · 2 across the 1 of their papers we have counts for
5 papers
Learning emergent PDEs in a learned emergent space
Felix P. Kemeth, Tom Bertalan, Thomas Thiem +4
We extract data-driven, intrinsic spatial coordinates from observations of the dynamics of large systems of coupled heterogeneous agents. These coordinates then serve as an emergen…
Coarse-grained and emergent distributed parameter systems from data
Hassan Arbabi, Felix P. Kemeth, Tom Bertalan +1
We explore the derivation of distributed parameter system evolution laws (and in particular, partial differential operators and associated partial differential equations, PDEs) fro…
Transformations between deep neural networks
Tom Bertalan, Felix Dietrich, Ioannis G. Kevrekidis
We propose to test, and when possible establish, an equivalence between two different artificial neural networks by attempting to construct a data-driven transformation between the…
Emergent spaces for coupled oscillators
Thomas N. Thiem, Mahdi Kooshkbaghi, Tom Bertalan +2
In this paper we present a systematic, data-driven approach to discovering "bespoke" coarse variables based on manifold learning algorithms. We illustrate this methodology with the…
On Learning Hamiltonian Systems from Data
Tom Bertalan, Felix Dietrich, Igor Mezić +1
Concise, accurate descriptions of physical systems through their conserved quantities abound in the natural sciences. In data science, however, current research often focuses on re…