Descripción del proyecto
Nuevos algoritmos para el aprendizaje automático del comportamiento físico en el espacio y el tiempo
A menudo se requieren simulaciones numéricas para estudiar el comportamiento de sistemas cuyos modelos matemáticos son demasiado complejos como para ofrecer soluciones analíticas. Al estudiar estos fenómenos, es necesario tener en consideración dimensiones espaciales que son difíciles de calcular y almacenar por lo que respecta a los recursos necesarios para hacerlo. El proyecto SpaTe, financiado con fondos europeos, tiene por objeto desarrollar nuevos algoritmos para inferir funciones espaciotemporales, lo que permite también la construcción de representaciones eficaces que, según los desarrolladores, controlarán su complejidad y su elevada dimensionalidad. En última instancia, SpaTe permitirá conocer mejor el mundo físico que nos rodea y ofrecerá importantes aplicaciones prácticas que van desde medios sociales de comunicación hasta coches autónomos.
Objetivo
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.
Ámbito científico
Palabras clave
Programa(s)
Régimen de financiación
ERC-COG - Consolidator GrantInstitución de acogida
80333 Muenchen
Alemania