Descrizione del progetto
Nuovi algoritmi per l’apprendimento automatico del comportamento fisico nello spazio e nel tempo
Spesso viene richiesto alle simulazioni numeriche di studiare il comportamento di sistemi, i cui modelli matematici sono troppo complessi per fornire soluzioni analitiche. Nello studio di questo fenomeno, è necessario considerare dimensioni spaziali difficili da calcolare e archiviare in termini di risorse necessarie. Il progetto SpaTe, finanziato dall’UE, si propone di sviluppare nuovi algoritmi per dedurre funzioni spazio-temporali, consentendo inoltre la creazione di rappresentazioni efficienti che, secondo gli sviluppatori, ne addomesticheranno la complessità e l’elevata dimensionalità. Infine, SpaTe consentirà una migliore comprensione del mondo fisico attorno a noi e offrirà applicazioni sostanziali pratiche che spaziano dalle applicazioni dei social media alle automobili a guida autonoma.
Obiettivo
Numerical simulations are of tremendous importance for a wide range of scientific disciplines and commercial enterprises. For the majority of these natural phenomena, we not only need to consider the three spatial dimensions, but additionally we need to resolve how these phenomena develop over time. Thus, most natural simulations inherently need to resolve four dimensional functions, and most effects at human scales require fine discretizations along all four axes. As a consequence, these functions require large amounts of resources to compute and store. This problem becomes even more pronounced with the advent of data-driven techniques and machine learning. The learning algorithms effectively add additional dimensions, and the complexity and dimensionality of the corresponding functions explains the current lack of data-driven algorithms for space-time functions despite their enormous potential. Within this research project I plan to address the fundamental difficulties that arise in this setting: I will develop novel algorithms to infer spatio-temporal functions, and to construct efficient representations to tame their complexity and high dimensionality. This project combines numerical simulations with computer vision, and machine learning, and has the potential to radically change the way we work with physical simulations. Not only will it break new ground for fast and controllable VFX animations, but it will additionally facilitate the development of new ways to capture physical effects, in conjunction with algorithms to make physical predictions based on observations. Ultimately, this direction will allow us to better understand the physical world around us. It will help us to analyze sparse and ambiguous measurements such as videos and 3D scans automatically and reliably, with a vast range of practical applications from social-media apps to autonomous vehicles.
Campo scientifico
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-COG - Consolidator GrantIstituzione ospitante
80333 Muenchen
Germania