1 code implementation • 6 Mar 2023 • Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
Here we present $\Phi$-SO, a Physical Symbolic Optimization framework for recovering analytical symbolic expressions from physics data using deep reinforcement learning techniques by learning units constraints.
1 code implementation • 26 May 2023 • Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
New large observational surveys such as Gaia are leading us into an era of data abundance, offering unprecedented opportunities to discover new physical laws through the power of machine learning.
1 code implementation • 12 Oct 2023 • Foivos I. Diakogiannis, Suzanne Furby, Peter Caccetta, Xiaoliang Wu, Rodrigo Ibata, Ondrej Hlinka, John Taylor
By adding this "temporal" dimension, we exploit strong signal correlations between successive observations in the sequence to reduce error rates.
1 code implementation • 4 Dec 2023 • Wassim Tenachi, Rodrigo Ibata, Thibaut L. François, Foivos I. Diakogiannis
We introduce "Class Symbolic Regression" a first framework for automatically finding a single analytical functional form that accurately fits multiple datasets - each governed by its own (possibly) unique set of fitting parameters.
1 code implementation • 6 Dec 2023 • Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
We present a framework for constraining the automatic sequential generation of equations to obey the rules of dimensional analysis by construction.