physgrad sin images as jpgs

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N_T 2025-02-17 13:49:12 +08:00
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@ -103,14 +103,13 @@ Chloe Paillard,
Kiwon Um,
and all github contributors!
## Citation
If you find this book useful, please cite it via:
```
@book{thuerey2021pbdl,
title={Physics-based Deep Learning},
author={N. Thuerey and B. Holzschuh and P. Holl and G. Kohl and M. Lino andP. Schnell and F. Trost},
author={N. Thuerey and B. Holzschuh and P. Holl and G. Kohl and M. Lino and Q. Liu and P. Schnell and F. Trost},
url={https://physicsbaseddeeplearning.org},
year={2021},
publisher={WWW}

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@ -126,7 +126,7 @@ The value of $\xi$ determines the conditioning of $\mathcal P$ with large $\xi$
Here's an example of the resulting loss landscape for $y^*=(0.3, -0.5)$, $\xi=1$, $\phi=15^\circ$ that shows the entangling of the sine function for $x_1$ and linear change for $x_2$:
```{figure} resources/physgrad-sin-loss.png
```{figure} resources/physgrad-sin-loss.jpg
---
height: 200px
name: physgrad-sin-loss
@ -137,7 +137,7 @@ Next we train a fully-connected neural network to invert this problem via equati
We'll compare SIP training using a saddle-free Newton solver to various state-of-the-art network optimizers.
For fairness, the best learning rate is selected independently for each optimizer.
When choosing $\xi=1$ the problem is perfectly conditioned. In this case all network optimizers converge, with Adam having a slight advantage. This is shown in the left graph:
```{figure} resources/physgrad-sin-time-graphs.png
```{figure} resources/physgrad-sin-time-graphs.jpg
---
height: 180px
name: physgrad-sin-time-graphs
@ -154,7 +154,7 @@ While the evaluation of the Hessian inherently requires more computations, the p
By increasing $\xi$ while keeping $\phi=0$ fixed we can show how the conditioning continually influences the different methods,
as shown on the left here:
```{figure} resources/physgrad-sin-add-graphs.png
```{figure} resources/physgrad-sin-add-graphs.jpg
---
height: 180px
name: physgrad-sin-add-graphs

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