15 papers
Physics-Informed Bayesian Optimization Warm-Starts for Sequential Convex Programming in Asteroid Surface Hopping
Baran Ekşi, Tufan Kumbasar
Surface hopping is an attractive mobility mode for small-body exploration, but designing fuel-optimal hops on asteroid 433~Eros requires solving a nonconvex optimal control problem…
Handover Analysis for Vehicular Communication with Explainability on the Fly
Ali Fuat Sahin, Semiha Tedik Başaran, Tufan Kumbasar
Handover (HO) management in vehicular networks requires fast and reliable decision-making under highly dynamic conditions. While machine learning (ML) approaches can improve HO det…
Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification
Mehmet Ali Ferah, Tufan Kumbasar
System identification (SysID) is critical for modeling dynamical systems from experimental data, yet traditional approaches often fail to capture nonlinear behaviors. While deep le…
xFODE: An Explainable Fuzzy Additive ODE Framework for System Identification
Ertugrul Kececi, Tufan Kumbasar
Recent advances in Deep Learning (DL) have strengthened data-driven System Identification (SysID), with Neural and Fuzzy Ordinary Differential Equation (NODE/FODE) models achieving…
xFODE+: Explainable Type-2 Fuzzy Additive ODEs for Uncertainty Quantification
Ertugrul Kececi, Tufan Kumbasar
Recent advances in Deep Learning (DL) have boosted data-driven System Identification (SysID), but reliable use requires Uncertainty Quantification (UQ) alongside accurate predictio…
SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
Murat Furkan Mansur, Tufan Kumbasar
Nonlinear system identification must balance physical interpretability with model flexibility. Classical methods yield structured, control-relevant models but rely on rigid paramet…