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Physics-Informed Neural Networks (PINNs) - Chris Rackauckas
Physics-informed neural networks (PINNs) are an increasingly powerful way to solve partial differential equations, generate digital twins, and create neural surrogates of physical models. In this podcast, Chris is talking about PINNs, what they are, how we can understand them and what mathematical properties PINNs have.
Galaxy-like Mouse Brain Visualization
The Anatomy of a Dynamical System
Dynamical systems are how we model the changing world around us. This video explores the components that make up a dynamical system.
1989 Computational Fluid Dynamics Highlights
For the upcoming podcast I am very happy to welcome Anna Lembke to my show!
Dr. Lembke received her undergraduate degree in Humanities from Yale University and her medical degree from Stanford University. She is currently Professor and Medical Director of Addiction Medicine, Stanford University School of Medicine. She is also Program Director of the Stanford Addiction Medicine Fellowship, and Chief of the Stanford Addiction Medicine Dual Diagnosis Clinic. She is a diplomate of the American Board of Psychiatry and Neurology, and a diplomate of the American Board of Addiction Medicine.
Dr. Lembke recently appeared on the Netflix documentary The Social Dilemma, an unvarnished look at the impact of social media on our lives.
📚 Her new book: Dopamine Nation: Finding Balance in the Age of Indulgence
In this podcast, we talked about:
Drugs & Social Media 💊 💻
Understanding the Brain 🧠
Developing cross-addictions 🚬
Tech companies and their contribution to addiction 💻
and much more…
Went Live This Week
Book of the Week
An Introduction to Statistical Learning: with Applications in R
The 2nd edition of An Introduction for Statistical Learning (with R examples) is out! The book includes code examples with #R.
The second edition includes the following topics:
- Sparse methods for classification and regression
- Decision trees
- Support vector machines
The following topics were added to the 2nd edition:
- Deep learning
- Survival analysis
- Multiple testing
- Naive Bayes and generalized linear models
- Bayesian additive regression trees
- Matrix completion
“We owe almost all our knowledge not to those who have agreed but to those who have differed.” – Charles Caleb Colton
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Meme of the Week
Can you please not…
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See you next week and in the meantime, make sure to keep engineering your mind!