From 6c362ee635804f55ba52f123ea539e58e981e9a6 Mon Sep 17 00:00:00 2001 From: "Klamkin, Michael" Date: Mon, 17 Nov 2025 11:06:18 -0500 Subject: [PATCH 1/2] description class11 --- class11/class11.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/class11/class11.md b/class11/class11.md index e68f03d..fe3d9bd 100644 --- a/class11/class11.md +++ b/class11/class11.md @@ -6,5 +6,4 @@ --- -Add notes, links, and resources below. - +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. \ No newline at end of file From 41b7345a3edcb0f326c7c241b063bcee5e591e2a Mon Sep 17 00:00:00 2001 From: "Klamkin, Michael" Date: Mon, 17 Nov 2025 11:07:23 -0500 Subject: [PATCH 2/2] link --- class11/class11.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/class11/class11.md b/class11/class11.md index fe3d9bd..ac47eee 100644 --- a/class11/class11.md +++ b/class11/class11.md @@ -6,4 +6,6 @@ --- -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. \ No newline at end of file +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) \ No newline at end of file