AI RESEARCH
Differentiable Parameter Optimization for DAEs with State-Dependent Events
arXiv CS.LG
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ArXi:2605.05395v1 Announce Type: new Differential-algebraic equations (DAEs) with state-dependent events arise in systems whose continuous dynamics are constrained by algebraic equations and interrupted by mode changes, switching logic, impacts, or state reinitializations. Gradient-based parameter learning for such systems is challenging because algebraic variables are implicitly defined, event times depend on the parameters, and reset maps