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Emre Özkaya

Publications and source records attributed to Emre Özkaya.

5 recordsLinked to original sources
arXiv2026

Optimal Reward Shaping: Autonomous Car Parking Case Study

Designing effective reward functions for model-free reinforcement learning under non-holonomic constraints remains a persistent challenge, often resulting in severe local minima such as policy paralysis or over-conservative hazard avoidance. In this work, we present a parameterized reward shaping framework featuring coverage-gated alignment feedback, drive-direction switch regularization, and an aligned episode termination mechanism evaluated on an autonomous parallel parking task. Crucially, we show that environmental reward parameters and algorithmic hyperparameters are deeply co-dependent, requiring joint meta-optimization to achieve stable convergence. By employing surrogate-based Bayesian optimization, our co-optimized Deep Q-Network (DQN) agent resolves characteristic control failure modes, significantly outperforming uncalibrated baselines across both success rate and trajectory smoothness.

cs.LG↗
arXiv2023

Trailing-Edge Noise Reduction using Porous Treatment and Surrogate-based Global Optimization

Broadband noise reduction is a significant problem in aerospace and industrial applications. Specifically, the noise generated from the trailing edge of an airfoil poses a challenging problem with various proposed solutions. This study investigates the porous trailing edge treatment. We use surrogate-based gradient-free optimization and an empirical noise model to efficiently explore the design space and find the optimal porosity distribution. As a result, a predicted 8-10 dB reduction in the broadband 300-5000 Hz was achieved. Furthermore, the optimal design emphasizes the design space's complexity and global exploration's difficulty. Further, the optimal design presents a low porous solution while constituting significant noise reduction.

physics.flu-dyn↗
arXiv2018

Global Aerodynamic Design Optimization via Primal-Dual Aggregation Method

Global aerodynamic design optimization using Euler or Navier-Stokes equations requires very reliable surrogate modeling techniques since the computational effort for the underlying flow simulations is usually high. In general, for such problems, the number of samples that can be generated to train the surrogate models is very limited due to restricted computational resources. On the other hand, recent developments in adjoint methods enable nowadays evaluation of gradient information at a reasonable computational cost for a wide variety of engineering problems. Therefore, a much richer data set can be collected using an adjoint solver in a Design of Experiment framework. In the present work, we present a novel aggregation method, which enables the state of the art surrogate models to incorporate extra gradient information without causing overfitting problems. Therefore, accurate surrogate models with relatively large number of design parameters can be trained from a small set of samples. We also present results two well known benchmark design optimization problems showing efficiency and robustness of the new method.

math.OC↗
arXiv2018

On the Stability of Gradient Based Turbulent Flow Control without Regularization

In this paper, we discuss selected adjoint approaches for the turbulent flow control. In particular, we focus on the application of adjoint solvers for the scope of noise reduction, in which flow solutions are obtained by large eddy and direct numerical simulations. Optimization results obtained with round and plane jet configurations are presented. The results indicate that using large control horizons poses a serious problem for the control of turbulent flows due to existence of very large sensitivity values with respect to control parameters. Typically these sensitivities grow in time and lead to arithmetic overflow in the computations. This phenomena is illustrated by a sensitivity study performed with an exact tangent-linear solver obtained by algorithmic differentiation techniques.

math.OC↗