Advanced Numerics for Uncertainty and Bayesian Inference in Science

Descrizione del progetto

Perché l’intelligenza artificiale deve imparare il linguaggio della fisica

La scoperta scientifica moderna opera in due mondi frammentati: il vasto regno empirico dei big data e dell’apprendimento automatico e il regno preciso e meccanicistico delle simulazioni fisiche. Questi due approcci si scontrano, portando ad analisi inefficienti e all’incapacità di tenere pienamente conto dell’incertezza quando si combinano teoria e osservazione. Questo progetto finanziato dal CER intende colmare questo divario. Introduce i Metodi Numerici Probabilistici come linguaggio unificante, che ridefinisce tutti i calcoli come una forma di inferenza. Questo quadro olistico consentirà ai ricercatori di fondere in modo robusto i dati sperimentali con le leggi fisiche consolidate. Sviluppando un linguaggio semantico per tutti i tipi di conoscenza, il progetto promette di creare una cassetta degli attrezzi per la scienza quantitativa, inaugurando una nuova era di scoperta robusta.

Obiettivo

"Scientific knowledge enters computers through data on the one side, and laws of nature – implicit equations like differential equations and symmetries – on the other. They both provide information, empirical and mechanistic, respectively, crucial to the deduction of new insights. But the algorithms that operate on these sources of information stem from different communities and different eras: machine learning – ""big data"" – on the one hand, and simulation methods – high performance computing – on the other. One of the problems that arise from this disconnect is that inferring latent forces that drive dynamical systems from data requires ""shoehorning"" different algorithms together in inefficient optimization loops; another one is that uncertainty from discretization and emulation is not fully tracked.
Probabilistic numerical methods have emerged over the last decade as a holistic view on computation as inference. They provide a unifying language that can leverage empirical and mechanistic information. This proposal outlines a research program to complement and scale probabilistic numerical methods to enrich the quantitative scientist's toolbox along three axes: First, unifying uncertainty from empirical and computational knowledge in one common formalism, which allows the direct and robust combination of simulation and experimentation. Second, developing a rich and practical semantic language for the description of different types of knowledge – mechanistic, empirical, practical. Third, significant computational efficiency gains achieved by managing the computational process globally, instead of as a series of black boxes. Real scientific tasks will provide benchmarks and ensure practical relevance. An open-source software toolbox, complemented by regular summer schools, will ensure that the results reach their audience. As a result, ANUBIS will start a genuinely novel kind of quantitative scientific analysis at the intersection of simulation and machine learning."