Bastian Zapf: Physics-informed machine learning in PyTorch
Abstract: Physics-informed neural networks (PINNs) are a new and promising methodology to combine deep learning with partial differential equations (PDE). PINNs extend deep neural networks by regularizing their output to fulfill any given PDE, allowing to solve both forward and inverse PDE problems utilizing high-performance machine learning libraries such as Tensorflow and PyTorch. This talk will give a short introduction to PINNs and provide a detailed, tutorial-style code demonstration on their implementation in PyTorch.
This talk is part of the Mechanics Lunch Seminar series. That means 20min talks plus discussion in an informal setting.
Zoom: To obtain the Zoom meeting details please contact Timo Koch (timokoch at math.uio.no).