Update app.py

app.py

@@ -1313,10 +1313,12 @@ with gradio.Blocks() as demo:
             with gradio.Column():
                 gradio.Markdown("# Toward the Rapid Design of Engineered Systems Through Deep Neural Networks")
                 gradio.HTML("Christopher McComb, Carnegie Mellon University")
-                gradio.Markdown("Additive manufacturing is advantageous for producing lightweight components while maintaining function and form. This ability has been bolstered by the introduction of unit lattice cells and the gradation of those cells. In cases where loading varies throughout a part, it may be necessary to use multiple lattice cell types, also known as multi-lattice structures. In such structures, abrupt transitions between geometries may cause stress concentrations, making the boundary a primary failure point; thus, transition regions should be created between each lattice cell type. Although computational approaches have been proposed, smooth transition regions are still difficult to intuit and design, especially between lattices of drastically different geometries. This work demonstrates and assesses a method for using variational autoencoders to automate the creation of transitional lattice cells. In particular, the work focuses on identifying the relationships that exist within the latent space produced by the variational autoencoder. Through computational experimentation, it was found that the smoothness of transition regions was higher when the endpoints were located closer together in the latent space.")
+                gradio.Markdown("__Abstract__: The design of a system commits a significant portion of the final cost of that system. Many computational approaches have been developed to assist designers in the analysis (e.g., computational fluid dynamics) and synthesis (e.g., topology optimization) of engineered systems. However, many of these approaches are computationally intensive, taking significant time to complete an analysis and even longer to iteratively synthesize a solution. The current work proposes a methodology for rapidly evaluating and synthesizing engineered systems through the use of deep neural networks. The proposed methodology is applied to the analysis and synthesis of offshore structures such as oil platforms. These structures are constructed in a marine environment and are typically designed to achieve specific dynamics in response to a known spectrum of ocean waves. Results show that deep learning can be used to accurately and rapidly synthesize and analyze offshore structure.")
             with gradio.Column():
                 download = gradio.HTML("<a href=\"https://huggingface.co/spaces/cmudrc/wecnet/resolve/main/McComb2019_Chapter_TowardTheRapidDesignOfEngineer.pdf\" style=\"width: 60%; display: block; margin: auto;\"><img src=\"https://huggingface.co/spaces/cmudrc/wecnet/resolve/main/coverpage.png\"></a>")
     
+    gradio.Markdown("When designing offshore structure, like [wave energy converters](https://www.nrel.gov/news/program/2021/how-wave-energy-could-go-big-by-getting-smaller.html), it's important to know what forces will be placed on the structure as waves come at different speeds. Likewise, if we have some idea of how we want the structure to respond to different waves, we can use that to guide the design of the shape of the structure. We call the first process _Analysis_, and the second process _Synthesis_. This demo has ML models that do both, very quickly.")
+    
     with gradio.Tab("Analysis"):        
                  
         with gradio.Row():