Paper reduces hyperparameters in mixed-categorical Gaussian processes for green aircraft optimization.
problem High-dimensional mixed-categorical Gaussian processes with many hyperparameters.
method Innovative dimension reduction algorithm using partial least squares regression.
result Significant reduction in fuel consumption (439 kg) for a green aircraft.
Models for predicting aircraft motion are an important component of modern aeronautical systems. These models help aircraft plan collision avoidance maneuvers and help conduct offline performance and safety analyses. In this article, we develop a method for learning a probabilistic generative model of aircraft motion i…
In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of information about military and government movements. Ty…
Paper uses diffusion models to design electric aircraft quickly.
problem Designing electric aircraft efficiently and accurately.
method Simulation-based inference with hierarchical diffusion models.
result Rediscovers known aircraft design trends and laws.
Machine learning improves aircraft performance prediction by analyzing flight data.
problem Limited aircraft performance models based on industry-wide guidelines.
method Use machine learning on flight data to estimate drag and lift coefficients.
result Excellent accuracy in real-life data, supporting aerodynamics principles.
Bayesian optimization reduces computational effort in aircraft design optimization.
problem High computational cost in industrial aircraft design optimization.
method Constrained Bayesian optimization (Super Efficient Global Optimization with Mixture of Experts)
result Significant computational efficiency improvements over existing Isight optimizers.
A modern aircraft may require on the order of thousands of custom shims to fill gaps between structural components in the airframe that arise due to manufacturing tolerances adding up across large structures. These shims are necessary to eliminate gaps, maintain structural performance, and minimize pull-down forces req…
Bayesian optimization improves with transfer learning for aircraft design.
problem Cold start problem in Bayesian optimization for aircraft design.
method Ensemble of surrogate models using transfer learning in a constrained Bayesian optimization framework.
result Significant improvement in convergence and prediction accuracy.
Optimizes electric aircraft deployment for Canadian aviation to reduce emissions.
problem Limited fleet capacity and operational structure hinder electric aircraft transition.
method Multi-period mixed-integer linear programming (MILP) framework.
result Electric aircraft can reduce emissions by over 70% within five years.
This paper explores using a Long short-term memory (LSTM) based sequence autoencoder to learn interesting features for detecting surveillance aircraft using ADS-B flight data. An aircraft periodically broadcasts ADS-B (Automatic Dependent Surveillance - Broadcast) data to ground receivers. The ability of LSTM networks …
A federated learning framework improves RUL prognosis for aircraft engines without sharing data.
problem Limited run-to-failure data samples for accurate RUL prognosis.
method Federated learning framework, decentralized validation, and robust aggregation methods.
result The federated learning framework leads to more accurate RUL prognosis for five out of six airlines.
Air traffic control is a real-time safety-critical decision making process in highly dynamic and stochastic environments. In today's aviation practice, a human air traffic controller monitors and directs many aircraft flying through its designated airspace sector. With the fast growing air traffic complexity in traditi…
New method optimizes aircraft design with reduced computation using multi-fidelity models.
problem Efficiently solve complex aircraft design problems with limited computational resources.
method Proposes novel multi-fidelity selection strategies that consider both objective and constraint information.
result Shows 86% to 200% more constraint compliant solutions with a limited budget. Adapts Bayesian optimization for mixed constraints in aircraft design.
problem Optimizing expensive black box functions with mixed constraints.
method Super efficient global optimization with upper trust bound for constraints, Gaussian process uncertainty, refinement procedure.
result Superior performance on aircraft design problem compared to state-of-the-art solvers.
Paper uses deep imitation learning to predict aircraft trajectories accurately.
problem Inefficient and costly Air Traffic Management system limits predictability.
method Generative Adversarial Imitation Learning framework with trajectory clustering and classification.
result Accurate predictions for entire trajectory stages, pre- and tactical.
VerifAI toolkit improves neural network-based aircraft taxiing system safety.
problem Improving safety of autonomous aircraft taxiing systems using neural networks.
method Unified approach to formal analysis and retraining of AI systems, including falsification, debugging, and retraining.
result Improved neural network performance and reduced failure cases in aircraft taxiing system.
In aircraft industry, market needs evolve quickly in a high competitiveness context. This requires adapting a given aircraft model in minimum time considering for example an increase of range or the number of passengers (cf A330 NEO family). The computation of loads and stress to resize the airframe is on the critical …
The introduction of automated flight control and management systems have made possible aircraft designs that sacrifice arodynamic stability in order to incorporate stealth technology intro their shape, operate more efficiently, and are highly maneuverable. Therefore, modern flight management systems are reliant on mult…
A Ph.D. thesis proposes an efficient approach for optimizing aircraft eco-design with high-dimensional mixed integer variables.
problem Optimizing aircraft eco-design with high-dimensional mixed integer variables.
method EGO optimization approach based on sequential enrichment of adaptive surrogate models, adapted for high-dimensional and mixed variable spaces.
result Effective parameterization tools significantly reduce the number of design variables, enabling optimization of complex aircraft concepts.
Unified framework for modeling hierarchical spaces in design problems.
problem Challenges in modeling hierarchical, conditional, heterogeneous, or tree-structured domains.
method Unified framework combining feature modeling and graph theory, introducing meta and partially-decreed variables.
result Demonstrated effectiveness on complex system design problems, including neural networks and green-aircraft.
Bayesian optimization reduces hyperparameters for mixed variable design problems.
problem Optimizing designs with a large number of mixed continuous, integer, and categorical variables.
method Adaptive dimension reduction using partial least squares for fewer hyperparameters.
result Significant improvement in performance compared to genetic algorithms.
Green startups in Italy survive longer than non-green ones.
problem Survival of innovative startups in Italy.
method Comparative analysis of green vs. non-green startups in Italy, 2009-2018.
result Green startups are more than twice as likely to survive than non-green ones.
One approach to designing decision making logic for an aircraft collision avoidance system frames the problem as a Markov decision process and optimizes the system using dynamic programming. The resulting collision avoidance strategy can be represented as a numeric table. This methodology has been used in the developme…
Formula for Hadamard coefficients from Green's operators on spacetimes.
problem Calculating Hadamard coefficients from Green's operators on spacetimes.
method Developed formulas for diagonal values and integrals over the diagonal of Hadamard coefficients.
result Formulated analogues of Hadamard expansions and resolvents for Green's operators.
Green stocks show less factor exposure heterogeneity compared to brown stocks.
problem Exploring differences in factor exposure between green and brown stocks.
method Examined S&P 500 firms grouped by greenhouse gas emissions, analyzing factor exposure over 2014-2020.
result Green stocks have less factor exposure heterogeneity than brown stocks, except for the value factor.
Model shows government incentives boost green bond investment.
problem Increasing green investments through government incentives.
method Optimal incentives indexed on bond prices and covariation, applied to a portfolio of bonds.
result Method outperforms current tax-incentives systems in green investments.
Green functions on stationary varifolds established with inequalities and convergence results.
problem Establishing Green functions on stationary varifolds with inequalities and convergence.
method Extending Grüter and Widman's method, constructing Green functions, using local Harnack inequality.
result Green functions converge for sequences of stationary varifolds converging with multiplicity one.
The paper derives expansions for Green's operators and resolvents using Hadamard methods.
problem Analyzing normally hyperbolic operators and their Green's functions.
method Hadamard expansions for powers of Green's operators and resolvents.
result Derives expansions involving Hadamard coefficients for advanced/retarded Green's operators.
A new classification method based on Minimum Spanning Trees
problem Improving classification in supervised learning
method Proposing a classification algorithm based on Minimum Spanning Trees
result The proposed method is effective and computationally efficient
The paper provides formulas for Hadamard coefficients using Green's operators.
problem Calculating Hadamard coefficients from Green's operators.
method Various methods including resolvents, powers of Green's operators, and product with the real line.
result Formulas for Hadamard coefficients in terms of Green's operators.
A new framework uses deep reinforcement learning to improve aircraft separation in busy airspace.
problem Improving aircraft separation in high-density, dynamic airspace constrained by human controllers.
method Proximal Policy Optimization with an attention network for distributed vehicle autonomy.
result The framework significantly reduces offline training time and increases performance.
The paper discusses methods to compute Green's function on algebraic surfaces using Schottky uniformization.
problem Computing Green's function on algebraic surfaces using Schottky uniformization.
method Investigates convergence of deformations of a formula related to Green's function.
result Provides insights into the geometric interpretation of the formula for Green's function.
A regression algorithm uses Green's function and covariance matrix for predictive distributions.
problem Regression and uncertainty quantification for machine learning.
method Green's function theory, Bayesian approach, covariance matrix of normalized Green's function.
result The covariance matrix provides predictive distributions with mean and confidence intervals.
A new approach for green investing in Indian markets considers environmental factors.
problem Identifying and managing climate risk in sustainable investing.
method Combining ESG ratings with modern portfolio theory and scenario analysis.
result The green portfolio performs better than market returns, highlighting the importance of climate risk.
Study on p-Green functions on specific manifolds, proving monotonicity.
problem Monotonicity of p-Green functions on certain 3D manifolds. method Sharp monotonicity formula for p-Green functions along level sets. result Established monotonicity for 1<p<3 on specific manifolds. Margin trading and short selling boost green tech innovation in China.
problem Encouraging green technology innovation in Chinese companies.
method Quasi-experimental research using panel data of Chinese listed companies, double difference model.
result Margin trading and short selling increase green tech innovation significantly.
On compact surfaces, a Green-Wasserstein inequality cannot be improved without the sqrt(log n) factor.
problem Can the Green-Wasserstein inequality be improved without the sqrt(log n) factor?
method Contradiction proof using second-moment estimates and semi-discrete random matching asymptotics.
result It is impossible to remove the sqrt(log n) factor in the inequality on any compact connected surface.
We study several quantities associated to the Green's function of a multiply connected domain in the complex plane. Among them are some intrinsic properties such as geodesics, curvature, and L2-cohomology of the capacity metric and critical points of the Green's function. The principal idea used is an affine scaling…
In this paper, we define the Green function for the Dirac operator under two local boundary conditions: the condition associated with a chirality operator (also called the chiral bag boundary condition) and the $\MIT$ bag boundary condition. Then we give some applications of these constructions for each Green function.…
CBMs improve interpretability in RUL prediction for aircraft engines.
problem Lack of interpretability in deep learning models for asset prognostics.
method Concept Bottleneck Models (CBMs) for RUL prediction.
result CBMs achieve comparable or superior performance to black-box models while being more interpretable.
The paper analyzes risk spillovers between AI ETFs, AI tokens, and green markets.
problem Risk spillovers among AI ETFs, AI tokens, and green markets.
method R2 decomposition method
result AI ETFs and clean energy act as risk transmitters, while AI tokens and green assets act as receivers.
Green functions for GJMS operators on spheres derived, linking geometry and rigidity.
problem Deriving Green functions for GJMS operators on spheres.
method Explicit representation formulae derived using Gegenbauer polynomials.
result Spheres uniquely characterized by their Green functions, with strong rigidity theorems for n=3,4,5. Survey on rigidity and almost rigidity of Green functions in non-negative Ricci curvature spaces.
problem Rigidity and almost rigidity of Green functions in non-negative Ricci curvature spaces.
method Survey and observation on Cheeger-Yau inequality on RCD spaces.
result Observations on the Cheeger-Yau inequality and its applications.
The Green function on spheres in 3D implies the surface is a round sphere.
problem Verifying a conjecture about the Green function on spheres.
method Analyzing the Green function form and its properties on a sphere.
result Closed C2 embedded surfaces with the specified Green function are necessarily round spheres. Green bond leaks impact equity markets, altering investor reactions.
problem Green bond leaks affect equity market reactions.
method Identified 259 instances of pre-announcement leaks in 2,036 green bond headlines.
result News leaks significantly alter equity trading dynamics and investor reactions.
New estimates for Green's functions in varying Kähler metrics.
problem Uniform estimates for Green's functions in Kähler metrics.
method Broadening techniques to allow complex structure variation and removing assumptions.
result Uniform estimates for Green's functions in families of canonical Kähler metrics.
Challenge to separate Earth's magnetic field from vehicle's magnetic field for accurate navigation.
problem Separate Earth's magnetic field from vehicle's magnetic field for accurate magnetic navigation.
method Use machine learning (ML) and integrate physics of magnetic navigation (SciML) to remove aircraft magnetic field from total magnetic field.
result A model can be constructed to effectively remove aircraft magnetic field from the dataset.
Study asymptotics of Poisson kernel and Green's functions for fractional conformal Laplacian.
problem Asymptotics of Poisson kernel and Green's functions for fractional conformal Laplacian.
method Sharp expansions derived for the Poisson kernel and Green's functions near singularities.
result Sharp expansions of the Green's functions solve the first part of Kim-Musso-Wei's conjecture.