A data-driven approach predicts morphological development under structural instability.
problem Understanding and predicting spatiotemporal complexities of morphogenesis under structural instability.
method Machine-learning framework based on physical modeling of morphogenesis.
result Identification of key bifurcation characteristics and prediction of history-dependent development.
Hybrid model combines PCA and RNN for better aerospace stock price prediction.
problem Challenges in predicting stock prices of aerospace companies due to market uncertainty and complexity.
method Combination of Principal Component Analysis (PCA) and Recurrent Neural Networks (RNN).
result PCA improves both accuracy and efficiency of stock price prediction.
In this study, we analyze the aerospace stocks prices in order to characterize the sector behavior. The data analyzed cover the period from January 1987 to April 1999. We present a new index for the aerospace sector and we investigate the statistical characteristics of this index. Our results show that this index is we…
Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize sm…
Gaussian process priors are commonly used in aerospace design for performing Bayesian optimization. Nonetheless, Gaussian processes suffer two significant drawbacks: outliers are a priori assumed unlikely, and the posterior variance conditioned on observed data depends only on the locations of those data, not the assoc…
Optimizes neural network for aerodynamic predictions with Bayesian Optimization.
problem Building efficient and accurate predictive models for aerodynamic performance.
method Bayesian Optimization for hyper-parameter tuning of a lightweight neural network.
result Significant improvement in accuracy (MAPE drops from 0.1433% to 0.0163%) and efficiency.
Structural health monitoring is a condition-based field of study utilised to monitor infrastructure, via sensing systems. It is therefore used in the field of aerospace engineering to assist in monitoring the health of aerospace structures. A difficulty however is that in structural health monitoring the data input is …
Computer simulators are nowadays widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modeling, and manufacturing. One fundamental issue in the study of computer simulators is known as experimental design, that is, how to select the input settings where the compu…
In this paper, we consider a problem of failure prediction in the context of predictive maintenance applications. We present a new approach for rare failures prediction, based on a general methodology, which takes into account peculiar properties of technical systems. We illustrate the applicability of the method on th…
This paper reviews Gaussian process-based multi-fidelity techniques for different fidelity relationships.
problem Combining accurate and cheap models for complex system design.
method Gaussian process-based multi-fidelity modeling techniques for varying fidelity relationships.
result Comparison of techniques on analytical and aerospace engineering problems.
Bayesian Optimization using Gaussian Processes is a popular approach to deal with the optimization of expensive black-box functions. However, because of the a priori on the stationarity of the covariance matrix of classic Gaussian Processes, this method may not be adapted for non-stationary functions involved in the op…
Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outc…
Improved aircraft structure prediction using derivative-enhanced sparse Cholesky GP method.
problem Accurate real-time prediction of aircraft structure performance.
method Combining derivative data with a modified dynamic sparse Cholesky linear system solver.
result Improved prediction accuracy of aircraft structure performance.
Convolutional Neural Networks (CNN) are being actively explored for safety-critical applications such as autonomous vehicles and aerospace, where it is essential to ensure the reliability of inference results in the presence of possible memory faults. Traditional methods such as error correction codes (ECC) and Triple …
Proposes ICE-based metric for better understanding interactions in black-box models.
problem Misleading global sensitivity metrics in black-box models due to interaction effects.
method Individual Conditional Expectation (ICE) curves to compute feature importance and interactions.
result ICE-based metric provides richer insights into feature importance and interactions.
We consider the fusion of two aerodynamic data sets originating from differing fidelity physical or computer experiments. We specifically address the fusion of: 1) noisy and in-complete fields from wind tunnel measurements and 2) deterministic but biased fields from numerical simulations. These two data sources are fus…
Monte Carlo (MC) techniques are often used to estimate integrals of a multivariate function using randomly generated samples of the function. In light of the increasing interest in uncertainty quantification and robust design applications in aerospace engineering, the calculation of expected values of such functions (e…
Advances in the field of Machine Learning and Deep Neural Networks (DNNs) has enabled rapid development of sophisticated and autonomous systems. However, the inherent complexity to rigorously assure the safe operation of such systems hinders their real-world adoption in safety-critical domains such as aerospace and med…
Multipath is among the major sources of errors in precise positioning using GPS and continues to be extensively studied. Two Fast Fourier Transform (FFT)-based detectors are presented in this paper as GPS multipath detection techniques. The detectors are formulated as binary hypothesis tests under the assumption that t…
This paper learns state, dynamics, and filtering algorithms together for data assimilation.
problem Costly parameter tuning and inaccurate dynamics models hinder data assimilation algorithms.
method Auto-differentiable data assimilation framework that learns state, dynamics, and parameters via gradient-based optimization.
result Several data assimilation methods can be learned or tuned within this framework.
Bayesian fusion improves radar target recognition for UAVs.
problem Improving radar target recognition for UAVs using multistatic radar configurations.
method Proposes a fully Bayesian RATR framework using Optimal Bayesian Fusion (OBF) to aggregate classification probability vectors from multiple radars.
result Empirical results show that the OBF method significantly enhances classification accuracy compared to other fusion methods and single radar configurations.
We present a deep recurrent neural network architecture to solve a class of stochastic optimal control problems described by fully nonlinear Hamilton Jacobi Bellmanpartial differential equations. Such PDEs arise when one considers stochastic dynamics characterized by uncertainties that are additive and control multipli…
SAWAR improves SA models by making them more robust to data uncertainties.
problem Improving survival analysis models' robustness to data uncertainties.
method Adversarial robustness through Min-Max optimization with CROWN-IBP.
result SAWAR outperforms baseline methods and SOTA models across various metrics.
Digital twin technology has significant promise, relevance and potential of widespread applicability in various industrial sectors such as aerospace, infrastructure and automotive. However, the adoption of this technology has been slower due to the lack of clarity for specific applications. A discrete damped dynamic sy…
Predicting the outcomes of integrating Unmanned Aerial Systems (UAS) into the National Aerospace (NAS) is a complex problem which is required to be addressed by simulation studies before allowing the routine access of UAS into the NAS. This thesis focuses on providing 2D and 3D simulation frameworks using a game theore…
Deep learning detects corrosion in nuclear fuel canisters.
problem Ensuring safety and integrity of used nuclear fuel dry storage canisters.
method Residual neural networks (ResNets) for real-time corrosion detection of canister images.
result Deep learning approach accurately detects corrosion and classifies canisters as corroded or intact.
Novel digital twin for complex systems improves performance.
problem Lack of practical implementation details for stochastic nonlinear MDOF systems.
method Decouples time-scales, uses physics-based model, Bayesian filtering, and machine learning.
result Excellent performance of proposed digital twin framework validated by examples.
Enhanced multi-fidelity models improve digital twin accuracy and uncertainty quantification.
problem Lack of detailed application-specific data and inaccurate sensor data hinder surrogate model learning for digital twins.
method Proposes a multi-fidelity surrogate model framework integrating PCFE and GP, and deep-HPCFE with auto-regression schemes.
result Demonstrates improved accuracy and uncertainty quantification in digital twin systems.
Self-supervised learning improves RUL prediction with limited data in fatigue damage prognosis.
problem Limited labelled data for RUL prediction in fatigue damage prognosis.
method Pre-training deep learning models on unlabelled sensor data using self-supervised learning.
result Self-supervised pre-trained models significantly outperform non-pre-trained models in RUL prediction with scarce labelled data.
Hybrid Bayesian MOT uses neural networks to improve model aspects, achieving state-of-the-art performance.
problem Improving multiobject tracking performance across various scenarios.
method Hybrid approach combining neural network enhancements with Bayesian estimation and belief propagation.
result State-of-the-art performance in autonomous driving dataset evaluation.
TadGAN detects anomalies in time series data using GANs and LSTM.
problem Challenges in detecting anomalies in time series data, especially without labeled data.
method TadGAN uses Generative Adversarial Networks (GANs) with LSTM Recurrent Neural Networks to capture temporal correlations and compute anomaly scores.
result TadGAN outperforms 8 baseline methods in most cases, achieving the highest averaged F1 score.
Study defines and optimizes bank reliability using LR and PSO.
problem Lack of reliability concept in financial services.
method Logistic Regression (LR) for initial estimation, Particle Swarm Optimization (PSO) for optimization.
result Optimal financial ratios maximize bank reliability.
Bayesian SSI improves modal parameter uncertainty in operational systems.
problem Uncertainty in modal parameters due to stochastic operational systems and lack of forcing information.
method Proposes a Bayesian stochastic subspace identification (SSI) algorithm with a hierarchical probabilistic model and two inference schemes (Markov Chain Monte Carlo and variational Bayes).
result Posterior distributions over modal properties are obtained, showing lower variance for mean values coinciding with natural frequencies.
A digital twin for multi-scale systems uses physics-based and machine learning models.
problem Lack of application-specific details in digital twin technology.
method Strategically separates into physics-based and data-driven models; uses mixture of experts with Gaussian Process.
result Robust and accurate predictions at future time-steps for multi-scale systems.
A framework for faster, better infographic design by non-experts and experts alike.
problem Designing infographics is time-consuming and tedious for non-experts and even professionals.
method Semi-automated infographic framework for structured and flow-based designs, including automatic design ranking and customization options.
result Designers from all expertise levels can generate generic infographic designs faster than existing methods while maintaining quality.
The paper proposes a method to reliably select design algorithms for machine learning-guided design tasks.
problem Choosing the right design algorithm for machine learning-guided design tasks.
method Combining designs' predicted property values with held-out labeled data to reliably forecast characteristics of the label distributions produced by different design algorithms.
result The method is guaranteed to return design algorithms that yield successful label distributions.
Unified approach to experimental design using interlacing polynomials.
problem Experimental design problems, especially D/A/E-design and E-design.
method Unified deterministic approach using interlacing polynomials.
result Improved approximation guarantees for various experimental design objectives.
We propose a new low-cost machine-learning-based methodology which assists designers in reducing the gap between the problem and the solution in the design process. Our work applies reinforcement learning (RL) to find the optimal task-oriented design solution through the construction of the design action for each task.…
Design-by-Morphing creates radical airfoil designs without geometric constraints.
problem Design constraints limit airfoil design novelty and small changes.
method Design-by-Morphing (DbM) creates a search space without geometric constraints.
result DbM generates radical airfoils with remarkable lift-over-drag ratio and stall angle tolerance.
MO-PaDGAN generates diverse, high-performance designs with multiple metrics.
problem Challenges in generating diverse, high-performance designs with multiple metrics.
method MO-PaDGAN uses a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances.
result MO-PaDGAN expands the design space towards high-performance regions and generates new designs with high diversity and performances.
MCD automates counterfactual design searches for multi-modal tasks.
problem Designing for multi-objective goals and complex constraints.
method Model-agnostic counterfactual search method for multi-modal design modifications.
result MCD streamlines and automates counterfactual search, recommending effective design modifications.
PaDGAN generates diverse, high-quality designs with improved performance.
problem Lack of diversity and performance improvement in generated designs.
method Integrates Determinantal Point Processes for diversity and quality, using GAN framework.
result PaDGAN generates higher quality designs with better diversity and without mode collapse.
ANN with GA optimizes flexible disc design for lower mass and stress.
problem Design flexible disc elements for lower mass and stress without compromising torque transmission and misalignment.
method Artificial Neural Network (ANN) coupled with Genetic Algorithm (GA) for design exploration.
result Optimized designs meet specified criteria with minimum mass and stress.
New method for mixed-variable GSA improves material design efficiency.
problem Designing materials with both quantitative and qualitative variables.
method Integrates LVGP with Sobol' analysis for mixed-variable GSA.
result Accelerates exploration of novel MOF candidates in combinatorial design spaces.
Deep learning models enhance engineering design automation.
problem Design optimization and customization across various industries.
method Leveraging deep neural networks, GANs, VAEs, and DRL for design synthesis.
result Recent advances in DGMs show promise in structural optimization, materials design, and shape synthesis.
Efficient deep learning computing requires algorithm and hardware co-design to enable specialization: we usually need to change the algorithm to reduce memory footprint and improve energy efficiency. However, the extra degree of freedom from the algorithm makes the design space much larger: it's not only about designin…
DAD learns to design experiments quickly, outperforming traditional methods.
problem Real-time decision-making in sequential Bayesian experimental design.
method Amortized design network trained with contrastive information bounds.
result DAD outperforms alternative strategies on various problems.
Enhances scenario approach for certifying design properties post-design.
problem Certifying additional useful properties in designs not considered during the design phase.
method Two-level framework of appropriateness: baseline and post-design. Distribution-free upper bounds on risk derived.
result Distribution-free upper bounds on the risk of failing to meet post-design appropriateness.