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
Deep learning has proven to be a remarkably versatile and scalable technique for learning algorithms to process and interact with noisy, high-dimensional real-world data and systems. In deep learning, the backpropagation algorithm is used to adjust the parameters of a multi-layer (deep) neural network so that the network “learns” to perform desired mathematical functions. Here, I will discuss my work to adapt this procedure to train networks of controllable physical systems – physical neural networks – which directly learn physical functions, such as performing machine learning inference calculations [1]. I will present proof-of-concept PNNs we have constructed to perform image and audio classification, based on ultrafast nonlinear photonics, bulk analog electronics, and mechanics. Because PNNs learn physical transformations directly at the level of the hardware physics, without relying on predefined mathematical isomorphisms, they may harness noisy, analog physical processes for computation more efficiently and opportunistically than traditional approaches. More broadly, PNNs form the basis for a learning-based approach to the design and programming of physical devices, which may perform complex physical functions in non-digital domains, e.g. for sensing.
Watch on demand:
Recording will be uploaded after the seminar.