xFODE uses incremental states and fuzzy additive models trained with partitioning strategies to deliver accurate yet interpretable nonlinear dynamic models that match NODE and FODE performance on benchmarks.
Deep learning of dynamic systems using system identification tool- box™,
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
SOLIS recovers interpretable physical parameters for nonlinear systems by learning state-conditioned Quasi-LPV neural surrogates without assuming a fixed global equation.
xFODE+ is an interpretable type-2 fuzzy additive ODE model for system identification that produces prediction intervals with point predictions and retains physically meaningful states.
citing papers explorer
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xFODE: An Explainable Fuzzy Additive ODE Framework for System Identification
xFODE uses incremental states and fuzzy additive models trained with partitioning strategies to deliver accurate yet interpretable nonlinear dynamic models that match NODE and FODE performance on benchmarks.
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SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
SOLIS recovers interpretable physical parameters for nonlinear systems by learning state-conditioned Quasi-LPV neural surrogates without assuming a fixed global equation.
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xFODE+: Explainable Type-2 Fuzzy Additive ODEs for Uncertainty Quantification
xFODE+ is an interpretable type-2 fuzzy additive ODE model for system identification that produces prediction intervals with point predictions and retains physically meaningful states.