ODINN-SciML/DiffEqSensitivity-Review
A Review of Sensitivity Methods for Differential Equations
This project is a comprehensive review paper and accompanying code that helps researchers understand how to compute gradients of complex dynamical models. It takes a system of differential equations and a 'loss function' that measures how well the model performs, and provides different methods to calculate how changes in model parameters affect that loss. Scientists, engineers, and quantitative analysts who work with simulations based on differential equations would find this invaluable for optimizing their models.
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Use this if you need to understand or implement methods for calculating how sensitive your differential equation model's outputs are to changes in its input parameters.
Not ideal if you are looking for an out-of-the-box software tool to directly run sensitivity analysis without understanding the underlying mathematical methods.
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TeX
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MIT
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Nov 26, 2024
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