diff --git a/README.md b/README.md
index c0dc250..6696eaf 100644
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@@ -240,7 +240,7 @@ What's the underlying neural network architecture used in NeuralFoil?
 > * xxlarge: 5 layers, 256 wide.
 > * xxxlarge:5 layers, 512 wide.
 >
-> The domain knowledge embedding (the "physics-informed" part) happens primarily in a) encoding/decoding latent space choices, b) symmetry embedding, and c) how the model dynamically fuses a learned model and an empirical model, depending on the uncertainty of the learned model. NeuralFoil is "physics-informed", but notably not a [PINN](https://en.wikipedia.org/wiki/Physics-informed_neural_networks). ([To dispel a misconception that is common even among ML practitioners, "physics informed machine learning" is an umbrella term that extends far beyond just PINNs - see Steve Brunton's taxonomy here](https://youtu.be/JoFW2uSd3Uo).) NeuralFoil is an interesting case study about how sophisticated ML architectures (e.g., neural operators, GNNs) or loss function crafting (e.g., PINNs) are not always the only or best ways to embed physics domain knowledge into a model. In fact, simple strategies can often yield compelling tradeoffs, as measured by speed vs. accuracy vs. data efficiency vs. generalizability.
+> The domain knowledge embedding (the "physics-informed" part) happens primarily in a) encoding/decoding latent space choices, b) symmetry embedding, and c) how the model dynamically fuses a learned model and an empirical model, depending on the uncertainty of the learned model. NeuralFoil is "physics-informed", but notably not a [PINN](https://en.wikipedia.org/wiki/Physics-informed_neural_networks). ([To dispel a misconception that is common even among ML practitioners, "physics informed machine learning" is an umbrella term that extends far beyond just PINNs - see Steve Brunton's taxonomy here](https://youtu.be/JoFW2uSd3Uo).) NeuralFoil is an interesting case study about how full-field learning using sophisticated ML architectures (e.g., PINNs, neural operators, CNNs/GNNs) is not always the only or best way to embed physics domain knowledge into a model. In fact, simple strategies can often yield compelling tradeoffs, as measured by speed, accuracy, data efficiency, and generalizability.
 
 ## Acknowledgements