View source for 2025 CSDMS meeting-133

{{CSDMS meeting personal information template-2025
|CSDMS meeting first name=Donsub
|CSDMS meeting last name=Rim
|CSDMS meeting institute=Washington University in Saint Louis
|CSDMS meeting city=Saint Louis
|CSDMS meeting country=United States
|CSDMS meeting state=Missouri
|CSDMS meeting email address=rim@wustl.edu
}}
{{CSDMS meeting select clinics1 2025
|CSDMS_meeting_select_clinics1_2025=2) Simulating Glacier Flow with ICEPACK
}}
{{CSDMS meeting select clinics2 2025
|CSDMS_meeting_select_clinics2_2025=2) From Exploration to Publication: Geospatial Research in the Jupyter Ecosystem
}}
{{CSDMS meeting select clinics3 2025
|CSDMS_meeting_select_clinics3_2025=5) Will not attend a clinic this day
}}
{{CSDMS meeting abstract yes no 2025
|CSDMS meeting abstract submit 2025=Yes
}}
{{CSDMS meeting abstract poster Epub 2025
|CSDMS meeting poster Epub submit 2025=Poster
}}
{{CSDMS meeting abstract title temp2025
|CSDMS meeting abstract title=A Stability Analysis of Neural Networks and Its Application to Tsunami Early Warning
|Working_group_member_WG_FRG=Coastal Working Group, Cyberinformatics and Numerics Working Group, Hydrology Focus Research Group
}}
{{CSDMS meeting abstract template 2025
|CSDMS meeting abstract=Neural networks (NNs) enable precise modeling of complicated geophysical phenomena but can be sensitive to small input changes. In this work, we present a new method for analyzing this instability in NNs. We focus our analysis on adversarial examples, test-time inputs with carefully crafted human-imperceptible perturbations that expose the worst-case instability in a model's predictions. Our stability analysis is based on a low-rank expansion of NNs on a fixed input, and we apply our analysis to a NN model for tsunami early warning which takes geodetic measurements as the input and forecasts tsunami waveforms. The result is an improved description of local stability that explains adversarial examples generated by a standard gradient-based algorithm, and allows the generation of other comparable examples. Our analysis can predict whether noise in the geodetic input will produce an unstable output, and identifies a potential approach to filtering the input that enable more robust forecasting.
|CSDMS meeting posterPDF= Tsunami neuralnetwork stability.pdf
|CSDMS meeting posterPNG= Tsunami neuralnetwork stability.png
}}
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