Physics-informed neural networks (PINNs)
Вставка
- Опубліковано 8 лип 2024
- Speaker: Fergus Shone, PhD Researcher, University of Leeds
Do you have sparse, low-quality data, but a good understanding of the physical system you are modelling? Then physics-informed neural networks (PINNs) might be the machine learning tool for you!
Bio:
Fergus is a PhD researcher working with the Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) and the Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) at the University of Leeds. His research interests lie in the fields of physics-informed machine learning and super-resolution of in vivo cardiac flow data.
Great work Fergus! WE always use the self-adaptive weights of the Texas A&M group.
Nice work guys
Impressive!
Damn if you could do this with a freaking heart I hope I can manage to do it with a simple plane model. Thank you for sharing!!
We believe in you! Let us know how you get on 😃
Can anyone tell about what residual form infers at 4:40 in the slide
thanks
Hi Siva, the residual form simply involves moving all terms of the equation to one side, so we have F(x,u) = 0. If the ODE/PDE is satisfied then F(x,u) will equal zero, so minimising this term during training constrains the predicted variables to satisfy the residual, and thus the PDE/ODE. Hope that helps!
Can I use this in Economics? Econ has "conservation" in the long-run, but in the short-run wonky things happen.
Flash crash formula right here.
@@memegazer please elaborate
@@Septumsempra8818
So basically in theory you could train a model to take certain economic assumptions to predict market behavior...but unlike physically based models those assumptions are not necessarily as objectively robost.
So doing so, in theory if different competing models in the market used AI to guide their investment strategies...but those big firms made different assumptions...like for example that their firms model should beat the market...then that could easily spiral out of control if machines were making all the market decisions with goal of maximizing returns.
Congrats!!