112 citations · 151 across the 13 of their papers we have counts for
5 papers · 1 filter
NestyNet. IV. Laws Chosen by Nothing in Advance
Rodrigo Ibata, Wassim Tenachi, Foivos Diakogiannis +2
Differential-equation (DE) discovery tends to break down precisely where much of physics begins. Fields are coupled, governing laws are nonlinear in the state, amplitudes, coordina…
NestyNet. III. Symbolic Regression from Analytic Neural Surrogates
Rodrigo Ibata, Wassim Tenachi, Foivos Diakogiannis +2
Many physical laws are simple only after the right representation, decomposition or internal coordinate has been found, but discovering that structure from data is combinatorially…
NestyNet. II. Coherent Function-Space Posteriors from Scientific Neural Surrogates (or How to Avoid Expensive MCMC)
Rodrigo Ibata, Wassim Tenachi, Foivos Diakogiannis +2
Scientific analyses increasingly use flexible neural networks, but their thousands of correlated parameters make it challenging to interpret the associated uncertainties. Here we d…
NestyNet. I. Physics Functions Are Hard to Fit with Neural Networks: A Framework for Accurate Surrogates and Analytic Derivatives
Rodrigo Ibata, Wassim Tenachi, Foivos Diakogiannis +2
Many of the smooth functions that matter most in physics are precisely the ones that standard neural network methods struggle to fit accurately. Here we present NestyNet, a coupled…
Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws
Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
Symbolic Regression is the study of algorithms that automate the search for analytic expressions that fit data. While recent advances in deep learning have generated renewed intere…