Update app.py
Browse files
app.py
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@@ -326,12 +326,13 @@ with gradio.Blocks() as synthesis_demo3:
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with gradio.Blocks() as intro:
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title = gradio.Markdown("# Toward the Rapid Design of Engineered Systems Through Deep Neural Networks")
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with gradio.Row():
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with gradio.Column():
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gradio.Markdown("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 structures.\n\nThe paper linked to the left provides details about the implementation. This site contains demos of the trained networks.")
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with gradio.Column():
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download = gradio.HTML("<
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all_synthesis_demos = gradio.TabbedInterface([synthesis_demo, synthesis_demo2, synthesis_demo3], ["Spectrum from Dataset", "Spectrum from File", "Spectrum from DataFrame"])
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with gradio.Blocks() as intro:
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with gradio.Row():
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with gradio.Column():
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title = gradio.Markdown("# Toward the Rapid Design of Engineered Systems Through Deep Neural Networks")
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gradio.HTML("Christopher McComb<br/>Department of Mechanical Engineering<br/>Carnegie Mellon University")
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gradio.Markdown("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 structures.\n\nThe paper linked to the left provides details about the implementation. This site contains demos of the trained networks.")
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with gradio.Column():
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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>")
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all_synthesis_demos = gradio.TabbedInterface([synthesis_demo, synthesis_demo2, synthesis_demo3], ["Spectrum from Dataset", "Spectrum from File", "Spectrum from DataFrame"])
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