arXiv subjects

Arash Fath Lipaei

Publications and source records attributed to Arash Fath Lipaei.

4 recordsLinked to original sources

arXiv2026

Physics-Structured Surrogate Modeling and Conformal Robust Multipoint Optimization for Glider Wing Design

Aerodynamic design using surrogate assistance can lower the cost of concept design. Point accuracy, however, is not enough to ensure that the optimizer does not exploit any part of space which is uncertain or with low confidence. In this work we develop a locked physics-structured surrogate and robust multi-point framework for the early-stage design of glider wings. The framework utilizes a dataset of 150,000 Tornado vortex lattice simulations, which provides 16 continuous targets for the aerodynamics, root loads, and flight dynamics. A five-member dual-head ensemble distinguishes between similarity-based aerodynamic inputs and the physical structure required for dimensional dynamics, while exact decoder recovers dimensional forces and root-load proxy values. Split-conformal prediction gives simultaneous intervals for the 14 optimization outputs, while at the same time a nearest-neighbor support score limits the extrapolation. The in-distribution test resulted in a mean NRMSE of 0.0223, while the structured out-of-distribution test resulted in 0.0595. The global joint 95% intervals covered 94.71% of points in distribution and support conditioned calibration gave 91.08% coverage under structured shift. Three-speed search examined 16,384 geometries and kept 2,998 feasible designs, 198 of which are nondominated designs. After freezing 20 wings, 60 Tornado simulations were conducted. The 60 simulations resulted in a mean NRMSE of 0.0228, coverage for 58 of 60 operating points, and hard feasibility success for all 60. For all 20 wings, all three objective upper bounds are conservative. The primary contribution of this paper is the combination of structured multi-output learning, simultaneous calibration, support aware robust Pareto search and locked post-selection simulations.

cs.NE↗

arXiv2026

Fidelity-informed neural pulse compilation of a continuous family of quantum gates with uncertainty-margin analysis

We develop a fidelity-informed neural pulse-compilation framework for a continuous family of single-qubit gates on a three-qubit liquid-state nuclear magnetic resonance (NMR) processor. Instead of decomposing each target unitary into a sequence of calibrated basis gates, the method learns a direct map from the axis-angle parameters of an arbitrary U_2 in SU(2) operation to a piecewise-constant radio-frequency control sequence that implements the desired transformation. Training is performed end-to-end through the time-ordered propagator of the driven Hamiltonian using global-phase-insensitive unitary fidelity as the learning signal. We show numerically that a single model generalizes across a continuous range of gate parameters and experimentally validate representative compiled pulses on a benchtop three-qubit NMR device. In addition, we analyze sensitivity to structured perturbations in Hamiltonian and control parameters by introducing a prescribed uncertainty set and performing a comparative risk-aware redesign based on right-tail Conditional Value-at-Risk (RU-CVaR). This stage produces pulse solutions with broader tolerance margins within the chosen uncertainty model. The results demonstrate continuous pulse-level gate synthesis in an experimentally accessible setting and illustrate a hardware-aware compilation strategy that can be extended to other quantum platforms. While the uncertainty model considered here is tailored to NMR, the neural compilation and risk-aware optimization framework are general and may be useful in architectures where calibration overhead, parameter drift, or control constraints make repeated per-gate optimization costly.

quant-ph↗

arXiv2025

Development of Neural Network-Based Optimal Control Pulse Generator for Quantum Logic Gates Using the GRAPE Algorithm in NMR Quantum Computer

In this paper, we introduce a neural network to generate optimal control pulses for general single-qubit quantum logic gates, within a Nuclear Magnetic Resonance (NMR) quantum computer. By utilizing a neural network, we can efficiently implement any single-qubit quantum logic gates within a reasonable time scale. The network is trained by control pulses generated by the GRAPE algorithm, all starting from the same initial point. After implementing the network, we tested it using numerical simulations. Also, we present the results of applying Neural Network-generated pulses to a three-qubit benchtop NMR system and compare them with simulation outcomes. These numerical and experimental results showcase the precision of the Neural Network-generated pulses in executing the desired dynamics. Ultimately, by developing the neural network using the GRAPE algorithm, we discover the function that maps any single-qubit gate to its corresponding pulse shape. This model enables the real-time generation of arbitrary single-qubit pulses. When combined with the GRAPE-generated pulse for the CNOT gate, it creates a comprehensive and effective set of universal gates. This set can efficiently implement any algorithm in noisy intermediate-scale quantum computers (NISQ era), thereby enhancing the capabilities of quantum optimal control in this domain. Additionally, this approach can be extended to other quantum computer platforms with similar Hamiltonians.

quant-ph↗

arXiv2025

Model Predictive and Reinforcement Learning Methods for Active Flow Control of an Airfoil with Dual-point Excitation of Plasma Actuators

This study investigates the effectiveness of Model Predictive Control (MPC) and Reinforcement Learning (RL) for active flow control over a NACA 4412 airfoil near static stall at Reynolds number 4*10^5. By systematically evaluating these strategies, the research addresses a critical gap in optimizing excitation frequency and improving response time in flow control. The work contributes to understanding RL adaptability and performance versus MPC in aerodynamic flow separation control. Numerical simulations of the Reynolds Averaged Navier-Stokes equations with the Scale-Adaptive Simulation turbulence model are used. Dielectric Barrier Discharge plasma actuators in dual-point excitation mode control flow separation. The study evaluates adaptive MPC, temporal difference RL (TDRL), and deep Q-learning (DQL) for optimizing excitation frequency and expediting stabilization. An integrated signal processing DQL approach is also examined. Adaptive MPC achieved Cl = 1.60 at 110 Hz but struggled near physical limits. RL optimized excitation frequencies, reaching Cl = 1.62 in under 2.5 s at 100 or 200 Hz. The study presents a novel RL - MPC comparison for active flow control with DBD actuators, contrasting with prior work focusing on MPC or RL alone. Using an online learning framework, RL methods dynamically adapt to real-time conditions. Evaluating adaptive MPC and RL together in this setup yields new insights into comparative performance in dynamic environments.

physics.flu-dyn↗