SOURCE-LINKED INTELLIGENCE

A Probabilistic Integration Recurrent Neural Network for Scientific Applications

CORDIS · observation · Publication date unknown

A Probabilistic Integration Recurrent Neural Network for Scientific Applications Scientists have huge needs for better analysis tools. Machine learning is a powerful framework to provide outstanding tools, but the current large-scale neural networks are not adapted for scientific applications where datasets are limited. Probabilistic networks are a powerful alternative for smaller datasets. However, they have flaws that prevent them to work well for complex tasks. They notably have speed and accuracy limitations. This project aims to make a breakthrough in machine learning for scientific applications by developing a new physics-informed probabilistic neural network adapted for small datasets. The basic unit of our network overcomes the limitations of the current probabilistic methods by considering a recurrent Gaussian process and using an analytical integration method. The first objective of our project is to deploy several units that each represent a state in a neural network, and to consider abrupt transitions from o

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

413379.72

unit

EUR