Hi,
I’m considering proposing a *Symbolic Sensitivity Analysis* module for SymPy in GSoC 2025 and wanted to get your thoughts on how best to align it with SymPy’s goals. The idea is to introduce *sympy.sensitivity*, a module that systematically handles sensitivity analysis for parametric systems—both algebraic and differential. While SymPy provides differentiation through diff(), there’s no unified framework for studying how parameter variations affect system behavior. This module would: - *Compute symbolic sensitivities* (e.g., ∂f/∂p for parametric functions). - *Support ODE sensitivity analysis*, generating sensitivity equations alongside system dynamics. - *Extend to higher-order sensitivities* (Hessians, second-order effects). - *Provide tools for simplification and interpretation*, helping identify dominant parameters. This functionality would be valuable in control systems, optimization, and physics, where understanding parameter influence is crucial. Given SymPy’s existing tools in calculus, solvers, and physics, I see this as a natural extension—but I’d love to hear if there are specific considerations or integrations that would make it a stronger fit within the SymPy ecosystem. Would this be something worth pursuing for GSoC? Are there key challenges I should address in my proposal to improve its viability? Any insights would be really helpful! Best, Shubham Vaghasiya -- You received this message because you are subscribed to the Google Groups "sympy" group. To unsubscribe from this group and stop receiving emails from it, send an email to sympy+unsubscr...@googlegroups.com. To view this discussion visit https://groups.google.com/d/msgid/sympy/9124ccc0-446d-404e-b552-f836be0a1cf6n%40googlegroups.com.
