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+This lecture reviews reviews physics-informed neural networks (PINNs), a class of deep learning models that incorporate physical laws into their architecture/training. We will discuss the formulation of PINNs, their applications, and common pitfalls to avoid when using them. Since the field is still developing, we structure the chapter as a review of a few select papers with interesting approaches to applying PINNs for problems in control.
 
+The chapter can be accessed [online](https://learningtooptimize.github.io/LearningToControlClass/dev/class11/class11.html)
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