Introduction
This bi-monthly seminar series explores real-world applications of physics-informed machine learning (Φ-ML) methods to the engineering practice. They cover a wide range of topics, offering a cross-sectional view of the state of the art on Φ-ML research, worldwide.
Participants have the opportunity to hear from leading researchers and learn about the latest developments in this emerging field. These seminars also offer the chance to identify and spark collaboration opportunities.
About the event
Backpropagation is the most widely used method for training Neural Networks. It has proven its effectiveness across a wide array of contexts, facilitating the efficient optimization of deep learning models. However, it exhibits certain weaknesses in specific scenarios that must be addressed to broaden the applicability of AI strategies in real-world situations. This is especially true in the integration of Deep Learning (DL) strategies within complex frameworks that deal with physics-related problems. Challenges such as the incorporation of non-differentiable components within neural architectures, or the implementation of distributed learning on heterogeneous devices, are just a few examples of the hurdles faced by researchers in the field.
Inspired by one of the recent works of Geoffrey Hinton, the Locally Backpropagated Forward Forward training strategy is a novel approach that merges the effectiveness of backpropagation with the appealing attributes of the Forward-Forward algorithm. This combination aims to provide a viable solution in contexts where traditional methods show limitations.
Watch on demand:
Recording will be uploaded after the seminar.
