Conferences  >  Mathematics  >  Neural Networks and Artificial Intelligence, Machine Learning  >  Spain

Computational and technological developments that incorporate natural language and reasoning methods are proliferating. Adequate coverage encounters difficult problems related to the phenomena of partiality, underspecification, perspectives of agents, and context dependency. These phenomena are signature features of information in nature, natural languages, and reasoning.

MODE (for Machine-learning Optimized Design of Experiments) is a collaboration of physicists and computer scientists who target the use of differentiable programming in design optimization of detectors for particle physics applications, extending from fundamental research at accelerators, in space, and in nuclear physics and neutrino facilities, to industrial applications employing the technology of radiation detection. Our aim to develop modular, customizable, and scalable, fully differentiable pipelines for the end-to-end optimization of articulated objective functions that model in full the true goals of experimental particle physics endeavours, to ensure optimal detector performance, analysis potential, and cost-effectiveness.

Scientific Computing and Data Science,    Computational Physics and Numerical Simulation