It’s the time of the year. The large applied geoscientists conference is coming up, the EAGE Annual Meeting. This year the EAGE is coming to London. I’ll be there as well and I’ll have a couple of speaking engagements, I’d like to invite you along to.
I love the newly created ritual of creating an off-brand hackathon accompanying the EAGE annual event. This year, Agile* stepped down as an organizer, but still stepped up as a sponsor for the London hack.
I am planning to work on two general projects. On June first, there is a global TensorFlow Docs Sprint that I want to spend some time on.
But mainly I would like to work on the subsurface package, we started working on at Transform 2019. It will be a central package at the subsurface geoscience Python stack. So, maybe join in the fun?
I’ll present work from my collaboration with ETLP Team at Heriot Watt University on including physics in the neural network architecture. I think Lukas Mosser, Olivier Dubrule, Mark Thompson, and Duncan Irving have put a lot of thought in this event. I’m looking forward to an insightful and inspiring event. Just take a look at the (abridged) description:
📝 The workshop will discuss recently developed applications of ML, and the challenges and opportunities associated with the development of these applications in the petroleum industry. The first half of the workshop will include:
The second half of the workshop will be dedicated to:
My talk will be focusing on:
Geoscience data often have to rely on strong priors in the face of uncertainty. Additionally, we often try to detect or model anomalous sparse data that can appear as an outlier in machine learning models. These are classic examples of imbalanced learning. Approaching these problems can benefit from including prior information from physics models or transforming data to a beneficial domain. We show an example of including physical information in the architecture of a neural network as prior information. We go on to present noise injection at training time to successfully transfer the network from synthetic data to field data.
Check it out on ArXiv and come to the workshop.
Someone thought it’s a good idea for me to be on a stage and give career advice. I find this relatively funny, as I’m mainly bumbling my way around science, while being pleasant to work with. If you’d like to hear me speak anyways, come by the EAGE Career Advice Center.
On Tuesday, June 4th at 10:00, I hope to have a conversation on
On Wednesday, June 5th around lunchtime at 12:30, we’ll chat around
I’ll see you at ExCel in London!
Here at The Way of the Geophysicists, we have written about social justice before. Today… Read More
This Friday we're looking at a machine learning state-of-the-art Dashboard and also a new way… Read More
It sure is an interesting time. Apologies I kept you waiting with more Friday Faves,… Read More
Aaaand it's gone. It's starting out with one of my new projects and then a… Read More
I'm starting a new project, where I take concepts from machine learning for science and… Read More
It's the holiday season, so let's keep this Friday Fave short, with a fave that… Read More