Ozhan T.

New journal paper! Our paper "Low-thrust trajectory design for icy moons orbiters using multi-body techniques" is now available in Celestial Mechanics and Dynamical Astronomy. The approach outlined in this paper exploits #multibody dynamics theory, especially the mechanism of three-body resonant #gravityassist to construct a resonant hopping trajectory, which is then optimized using an indirect forward-backward shooting method. The proposed #trajectoryoptimization approach is applied to design a #lowthrust inter-moon transfer between two #icymoons in the #Saturn system, #Tethys and #Enceladus. The full-paper is also available on my ResearchGate:

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I'm pleased to share our latest open-access publication, "Physics-Based Reliability Modeling for Control Applications: Adaptive Control Allocation," in IEEE Access. https://lnkd.in/g-VjD-Tx This work presents a systematic approach to integrating physics-based reliability models within control systems. By addressing the limitations of conventional degradation models, we introduce a reliability framework that utilizes stress-derived variables, enabling real-time, physics-informed reliability assessments. Our approach is validated through simulations on a hexarotor UAV, demonstrating improved reliability predictions and adaptive workload distribution to extend system longevity. Special thanks to Joshua Desrosiers, Zac Heit, Ishimwe Uwantare, Andrew Goodchild Remoundos, and Anas Senouci for their dedicated work. #UAV #ReliabilityEngineering #PhysicsBasedModeling #AdaptiveControl #AerospaceEngineering #IEEE #Hexarotor #ControlSystems #ReliabilityModeling #UnmannedAerialVehicles #SafetyInnovation

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Check out our paper published in the International Journal of Multiphase Flow. In this study, led by my PhD student Amir Loyevsky, we developed a new Eulerian sectional approach for simulating erosion induced by particle-laden flows. Our new framework integrates several modules: carrier flow, dispersed particle dynamics, solid mechanics, and automatic remeshing, along with auxiliary empirical erosion and restitution models. This method provides an efficient numerical algorithm for better understanding particle-flow-structure interactions in complex flow environments. #CFD #FlowDynamics #ParticleErosion #ImpingingJets #NumericalModeling

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AI + Science is the future of science ! 🍾🥂 This is the main conclusion of Ms Animashree Anandkumar's amazing Keynote today at the AI4Science and Nobel Turing Challenge Initiative Conference in Singapore 👏👏👏 Other conclusions of her insightful work have been: - Principled algorithms for zero-shot generalization. - Operator learning extends neural networks to learning in infinite dimensional spaces. - Orders of magnitude speedup while maintaining accuracy. 🚀 🛸🚀🛸🚀🛸🚀🛸🚀🛸🚀 My personal take-away will be the powerful potential of Neural Operators over Neural Networks, in particular for research on Weather forecast & Climate prediction 😎😎😎 🚀🛸🚀🛸🚀🛸🚀🛸🚀🛸🚀 My 2-cents 😇

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Learn more about UCF Electrical and Computer Engineering Department Chair Reza Abdolvand, who approaches his work as an engineer, inventor and professor with the intent to make a lasting impact: https://bit.ly/3zI2rSU

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Connecting impulsive and finite-thrust fuel-optimal trajectories is a fundamental problem in the field of astrodynamics! My excellent Ph.D. student Keziban Şaloğlu and I have been working on this fascinating topic. I encourage you to check out the paper if this fundamental problem and its solutions are of interest to you. Paper Title: Impulsive to Low-Thrust Mapping for Optimal Trajectories With Thrust Magnitude Continuation Summary: #Impulsive trajectories are notably simpler to generate and optimize compared to their finite- and #lowthrust counterparts. A fundamental #astrodynamics problem is to convert impulsive solutions into low-thrust trajectories. We use thrust-magnitude #continuation, in a reverse manner, to generate #fueloptimal low-thrust trajectories corresponding to a specific/desired value of thrust magnitude. However, the incipient impulsive solutions are minimum-impulse trajectories. Since the incipient impulsive solutions are optimal with respect to the total delta-v, the generated low-thrust solutions are mapped to extremal finite-thrust trajectories. Two notable features of the proposed method are: 1) guessing the non-intuitive #costates is completely circumvented, and 2) multiple-impulse trajectories can be converted into multiple-revolution low-thrust trajectories. The results demonstrate the robustness of the proposed impulsive-to-low-thrust mapping method, showing that the fuel-optimal low-thrust solution is unique for the specified total number of orbital revolutions, particularly when limited to a single orbital revolution on each phasing orbit. AIAA link: https://lnkd.in/ejq__MFX ResearchGate link: https://lnkd.in/e7jPamMq #Aerospace Auburn University Auburn University, Samuel Ginn College of Engineering

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Check out this cool addition to ArXiv, the open source scientific paper repository, which adds comments. Called alphaxiv, you can either change the URL of any arxiv.org paper to alphaxiv.org and see the comments or load a Chrome plug in that automatically adds them. Comes from Stanford student @ahmadrehaan. I hope this is incorporated in regular ArXiv soon!

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Humbled to share our latest research on "High-Resolution Safety Verification for Evasive Obstacle Avoidance in Autonomous Vehicles," now published in the IEEE Open Journal of Vehicular Technology. Our paper introduces a novel functional safety verification method tailored for evasive maneuvers in Autonomous Vehicles (AVs), drawing inspiration from the #ISO26262 Hazard Analysis and Risk Assessment (HARA) framework. By focusing on critical scenarios such as evasive maneuvers, our approach significantly enhances the resolution of safety assessments, thereby contributing to the advancement of safer and more sustainable autonomous transportation. This research builds upon our patented "Safe Agile Hazard Avoidance System for Autonomous Vehicles" (US20240001964A1), which revolutionizes agile safety responses in real-world AV applications. This work was a continuation of my Ph.D. research at Rutgers University which was partially funded and sponsored by National Science Foundation (NSF) and my current collaborations with North Carolina Agricultural and Technical State University and Center for Regional and Rural Connected Communities (CR2C2). Dive into the full paper here: ⇒ https://lnkd.in/eCEQNCBQ Special thanks to Dr. Milad Khaleghi, Dr. Alireza Partovi, PhD, Dr. Alireza Abbaspour, Chaitanya Shinde Dr. Yashar Mousavi, Dr. Vahid Azimi, and Prof. Ali Karimoddini for their invaluable contributions that have made this work truly unique. We also extend our gratitude to the peer reviewers and editors for their insightful comments. Excited about the opportunity to further enhance AVs safety in collaboration. Feel free to contact me at aliasghar.arab@nyu.edu or mojarab@genauto.ai to explore potential collaborations. #AutonomousVehicles #SafetyEngineering #Innovation #GeneralAutonomy #GenAuto #GenAutoAI

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 A pre-print of my summer intern's recent paper, entitled “Domain decomposition-based coupling of operator inference reduced order models via the Schwarz alternating method”, is available on ArXiv here: https://lnkd.in/g8qZ94CA. The paper presents and evaluates an alternating Schwarz-based approach for coupling together subdomain-local reduced order models constructed via non-intrusive Operator Inference (OpInf) with each other and with subdomain-local full order models (FOMs).

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New article published: "HLoOP Hyperbolic 2-Space Local Outlier Probabilities" Hyperbolic geometry has recently garnered considerable attention in machine learning due to its ability to embed hierarchical graph structures with low distortions for further downstream processing. This paper introduces a simple framework to detect local outliers for datasets grounded in hyperbolic 2-space, which is referred to as Hyperbolic Local Outlier Probability (HLoOP).

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Our recent paper (with Eliot Nychka) introduces an alternative method for designing celestial navigation systems. Traditional designs often rely on variants of Kalman filtering to manage uncertainty probabilistically. In contrast, we propose an approach grounded in H2/Hinf theory and linear parameter-varying (LPV) systems theory, focusing on the development of LPV estimators that handle norm-bounded uncertainties, with specific applications to navigation in Cislunar space. The paper is available here: https://lnkd.in/gtZP97-P. The key takeaways are: 1. The navigation system's performance and stability are rigorously guaranteed by design. 2. Uncertainty is represented as norm-bounded rather than probabilistic. 3. The design incorporates the full nonlinear dynamics (in LPV form) without any approximations. 4. Sensing requirements and the observer design, ensuring user-specified accuracy, are jointly determined within an integrated convex optimization framework. We gratefully acknowledge the AFOSR DDIP program and Erik Blasch for funding this research (FA9550-22-1-0539). We also extend our thanks to the Sandra and William R. Wheeler '70 Graduate Fellowship in Aerospace Engineering. #AFOSR, #sda, #cislunar, #TAMU, #tamuengineering Texas A&M University Department of Aerospace Engineering

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I posted a new demo of everyday automation to reduce busywork in your day! This is a follow-up to my earlier example, which used the Matlab language. Here I give another demo of renaming a large list of files in a batch, this time using a Unix/Linux shell script. In Linux OS's, this is one of the first approaches to consider for this task, & there are certainly others. You might want to do this for a large set of photos, for example. To draft the code, I used a coding copilot tool from OpenAI (cited in the comments). This is a good exercise if you have not tried it yet. Here I could have written the code myself from scratch, but this method saved some time. I was able to sanity-check the draft code & make adjustments. Key point to remember: code generator tools are improving, but you most likely need your own expertise for adjustments. You can issue follow-up prompts to refine the result. These tools do NOT replace your own common sense or domain knowledge! ...at least for now. I plan to continue this series next time, sharing a few other methods to solve the same problem in different languages, such as Python, VBA (the Microsoft Office macro language) or the C language. Enjoy & good luck with your own projects! 😀 https://lnkd.in/eXHB2k9E https://lnkd.in/evEmNRKw [Transparency note: this post was written by hand using organic human thinking 👍 ]

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New research on using deep learning for radio signal classification! 📡🎓 In a recent paper entitled, "Over the Air Deep Learning Based Radio Signal Classification" Researchers Tim O'Shea, Tamoghna Roy and Charles Clancy conducted an in-depth study on using deep neural networks to classify radio communications signals. Some key findings: - Deep residual networks (ResNets) achieved state-of-the-art performance compared to baseline methods using higher order moments and gradient boosted trees. The ResNets were over 6 dB more sensitive! It - Training on 1 million examples improved high SNR accuracy by 5-20% compared to smaller datasets. Having sufficient training data is critical. - Models trained on synthetic data transferred well to over-the-air data, with only a 7% drop in accuracy. Fine-tuning the last few layers recovered most of this performance. - Classification performance held up well under various wireless channel impairments like frequency offset, symbol rate offset, and multi-path fading. The researchers open-sourced an extensive new dataset spanning 24 analog and digital modulations under a range of propagation scenarios. This will enable further R&D in this exciting area. Deep learning continues to show enormous promise for autonomous understanding of the RF spectrum for applications like interference monitoring, dynamic spectrum access, and more. This research helps quantify key factors like neural network architecture, training dataset requirements, and performance impacts of real-world channels. Read the full paper here: https://lnkd.in/gYsxMf-i #deeplearning #machinelearning #wirelesscommunications #ResNets #signalprocessing

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Recently read this very interesting paper on photon counting statistics with SPAD sensors by Atul Ingle et al. https://lnkd.in/e9cwYadS What's surprising is how the dead window of a SPAD pixel adaptively rejects photons at high flux, leading to sub-Poissonian statistics in its photon counting distribution. Attached are some notes on deriving the photon counting distribution for an idealized SPAD, based off the paper. I'd be curious if any SPAD experts can point me to a derivation of this distribution in the literature.

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You can definitely find an answer to your question in this list of tutorials: 1. SMT Tutorial (linear, quadratic, gaussian process, ...) 2. Efficient Global Optimization: How to start? 3. Bayesian Optimization - Efficient Global Optimization to solve unconstrained problems 4. Bayesian Optimization with noisy data 5. Multi-Fidelity Gaussian Process (With required nested sampling, Without noise, With noise, Without nested sampling) 6. PODI+I tutorial in SMT with global and local basis 7. PODI+I application to airfoil design 8. Kernel engineering tutorial in SMT 9. Kernel engineering application to aeroelasticity prediction 10. Explainability and conformal prediction (Warning: The explainability usage tutorial has been moved to SMTorg/smt-explainability) 11. LHS sampling (initial and expanded) 12. Gaussian Process Trajectory Sampling 13. Noisy Gaussian Process 14. Sparse Gaussian Process 15. Cooperative Components Kriging 16. Mixed-integer and mixed-hierarchical surrogate models (Warning: The Design Space usage tutorial has been moved to SMTorg/smt-design-space-ext) 17. Specific notebook associated to the SMT 2.0 Journal Paper (submitted) with a focus on mixed integer and mixed hierarchical surrogate models (continuous, discrete, categorical) 18. Mixed-Integer Gaussian Process and Bayesian Optimization to solve unconstrained problems with mixed variables (continuous, discrete, categorical) 19. Mixed-Integer Gaussian Process and Bayesian Optimization for Engineering application #AI4E #AI #aerospace #MDO #GaussianProcess #Optimization #Bayesian

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Can iterative methods for linear systems be efficient for gate-based quantum computers? The no-cloning theorem has often been pointed out as a barrier, since copies of iterates are required for conjugate gradient or Krylov subspace methods. But what if the iterative scheme is linear? Our new paper tackling this issue, qRLS, has been published today. We show that if the iterative scheme is linear, a quantum state encoding the iterate can indeed be prepared with exponentially fewer resources compared to classical computing. This implies that relaxation methods like Jacobi, Gauss-Seidel, etc. can be exponentially efficient on quantum computers. https://rdcu.be/dFGDq This paper aims to provide a foundation to build more advanced and refined iterative methods for quantum computers and provides explicit circuit descriptions for example problems. The question remains to decrease the number of iterations. For this we have proposed a quantum multigrid approach. This idea is built on the same footing, that all operations in the multigrid algorithm are linear. Stay tuned for more developments! https://lnkd.in/d22pVA4E #quantum #quantumcomputing #engineering #finiteelement #differentialequations #pdes

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Check out our latest journal article, titled "A recurrent neural network-based approach for ballistic coefficient estimation of resident space objects in Low Earth Orbit", which has been published in Advances in Space Research (ASR) Journal! The work has been carried out together with my PhD supervisors at University of Naples Federico II, Prof. Giancarmine Fasano and Prof. Roberto Opromolla, and Dr. Davide Amato, who supervised me during my visiting period at Imperial College London. The article proposes a recurrent neural network (RNN)-based approach for the estimation of the ballistic coefficient in Low Earth Orbit from astrometric data, thus supporting functions in #SSA such as accurate orbit prediction and collision avoidance. A particular RNN architecture has been adopted, i.e., the Gated Recurrent Unit, as it has been demonstrated to be cost efficient and to have good performance for the required application. The sensitivity of RNNs with respect to atmospheric model uncertainty has been analysed. The algorithm can handle unevenly spaced time-series data. The proposed neural network has been demonstrated to be robust with respect to orbital state errors. The applicability of the presented approach is tested and discussed using synthetic datasets generated with a high-accuracy numerical orbital propagator, reaching a mean percentage error of 10% in the estimation of the ballistic coefficient. If you are interested, you'll find below the link to the article! #aerospace #debris #spacedebris #machinelearning #deeplearning #neuralnetworks #ballisticcoefficient #university #work #research #spaceweather https://lnkd.in/dAvjCwQp

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If you’re interested in scientific machine learning for applied physics systems, check out our ‘Tutorial’ paper that was just published in APL Machine Learning!

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PyTorch Researchers Introduce an Optimized Triton FP8 GEMM (General Matrix-Matrix Multiply) Kernel TK-GEMM that Leverages SplitK Parallelization

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