4 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…