2025 CSDMS meeting-069: Difference between revisions
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In this work we focus on interpreting the neural network to try to help explain what it is calculating in a theoretically grounded way. The output of activation maximization, Fourier analysis, neuron ablation, and other interpretability techniques are complicated, but might imply that the network is detecting patterns that are geographically meaningful. This poster will present these interpretability results. | In this work we focus on interpreting the neural network to try to help explain what it is calculating in a theoretically grounded way. The output of activation maximization, Fourier analysis, neuron ablation, and other interpretability techniques are complicated, but might imply that the network is detecting patterns that are geographically meaningful. This poster will present these interpretability results. | ||
|CSDMS meeting posterPDF= Jmartin_poster_vertical_2025.pdf | |||
|CSDMS meeting posterPNG= Jmartin_poster_vertical_2025.png | |||
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Latest revision as of 11:33, 21 May 2025
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Browse abstracts
Neural Networks Can Infer Geomorphic Model Parameters From Topography, and We Might Be Able To Figure Out How!
Prior modeling work has found that in 2D models that combine stream incision with diffusive hillslope processes, valley spacing is strongly controlled by the relative rates of advective and diffusive processes (Perron et al. 2008). In this work we train a simple convolutional neural network to predict the ratio of the coefficient of stream erosion (K) and coefficient of diffusion (D) used to generate the model topography. Across a test set of 1800 model runs with different K and D values, the network had a normalized root mean square error of 0.03, showing that convolutional neural networks have significant promise for extracting complex and geomorphically meaningful topographic signatures.
In this work we focus on interpreting the neural network to try to help explain what it is calculating in a theoretically grounded way. The output of activation maximization, Fourier analysis, neuron ablation, and other interpretability techniques are complicated, but might imply that the network is detecting patterns that are geographically meaningful. This poster will present these interpretability results.
