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1Flight delay propagation inference in air transport networks using the multilayer perceptron
G. Chen, H. Fricke, O. Okhrin, and J. Rosenow. Journal of air transport management, (January 2024)Publisher Copyright: © 2023 Elsevier Ltd.
a month ago by @scadsfct
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FIS_scads
causality,
Granger
perceptron
propagation,
Multilayer
topic_engineering
Delay
FIS_scadscausality,Grangerperceptronpropagation,Multilayertopic_engineeringDelay
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1Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization
K. Dietrich, D. Vermetten, C. Doerr, and P. Kerschke. Proceedings of the Genetic and Evolutionary Computation Conference, page 1007 -- 1016. United States of America, Association for Computing Machinery (ACM), (Jul 14, 2024)Publisher Copyright: © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM..
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imported
topic_engineering
FIS_scadsimportedtopic_engineering
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2Dynamic tail risk forecasting: what do realized skewness and kurtosis add?
G. Gallo, O. Okhrin, and G. Storti. WorkingPaper, (Sep 20, 2024)
a month ago by @scadsfct
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topic_engineering
econ.EM
FIS_scadstopic_engineeringecon.EM
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1Copula modeling from Abe Sklar to the present day
C. Genest, O. Okhrin, and T. Bodnar. Journal of Multivariate Analysis, (May 2024)Publisher Copyright: © 2023.
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Copula
models,
modeling
Dependence
topic_engineering
FIS_scadsCopulamodels,modelingDependencetopic_engineering
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1Preface to the Special Issue “Copula modeling from Abe Sklar to the present day”
C. Genest, O. Okhrin, and T. Bodnar. Journal of Multivariate Analysis, (May 2024)
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imported
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1Graph based process models as basis for efficient data driven surrogates - Expediting the material development process
J. Gerritzen, A. Hornig, and M. Gude. WorkingPaper, Open Engineering Inc., (Nov 16, 2024)
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making
modeling,
decision
Surrogate
Data
development,
Process
topic_engineering
driven
FIS_scadsmakingmodeling,decisionSurrogateDatadevelopment,Processtopic_engineeringdriven
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1A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP
J. Gerritzen, A. Hornig, P. Winkler, and M. Gude. Computational Materials Science, (September 2024)Publisher Copyright: © 2024 The Author(s).
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Fiber
modeling,
area_architectures
plastics,
reinforced
Constitutive
Machine
identification
learning,
networks,
Parameter
topic_engineering
Neural
FIS_scadsFibermodeling,area_architecturesplastics,reinforcedConstitutiveMachineidentificationlearning,networks,Parametertopic_engineeringNeural
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1Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning
J. Gerritzen, A. Hornig, P. Winkler, and M. Gude. ECCM21 - Proceedings of the 21st European Conference on Composite Materials, 3, page 1252--1259. European Society for Composite Materials (ESCM), (Jul 2, 2024)21st European Conference on Composite Materials, ECCM 21 ; Conference date: 02-07-2024 Through 05-07-2024.
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Convolutional
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Fiber
area_architectures
neural
plastics
rate
dependency,
reinforced
Direct
Strain
Machine
learning,
networks,
identification,
parameter
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ConvolutionalFIS_scadsFiberarea_architecturesneuralplasticsratedependency,reinforcedDirectStrainMachinelearning,networks,identification,parametertopic_engineering
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1Developing a numerical modelling strategy for metallic pin pressing processes in fibre reinforced thermoplastics to investigate fibre rearrangement mechanisms during joining
B. Gröger, J. Gerritzen, A. Hornig, and M. Gude. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 238 (12): 2286--2298 (Nov 15, 2024)Publisher Copyright: © IMechE 2024..
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Eulerian,
Joining,
Lagrangian
simulation
composite,
topic_engineering
Arbitrary
FIS_scadsEulerian,Joining,Lagrangiansimulationcomposite,topic_engineeringArbitrary
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1Vessel-following model for inland waterways based on deep reinforcement learning
F. Hart, O. Okhrin, and M. Treiber. Ocean engineering, (August 2023)Publisher Copyright: © 2023 Elsevier Ltd.
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1Two-step dynamic obstacle avoidance
F. Hart, M. Waltz, and O. Okhrin. Knowledge-based systems, (Oct 25, 2024)Publisher Copyright: © 2024 The Author(s).
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Dynamic
Local
learning
Deep
Supervised
planning,
path
learning,
avoidance,
reinforcement
topic_engineering
obstacle
FIS_scadsDynamicLocallearningDeepSupervisedplanning,pathlearning,avoidance,reinforcementtopic_engineeringobstacle
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1Dancing to the State of the Art?: How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem
J. Heins, L. Schäpermeier, P. Kerschke, and D. Whitley. Parallel Problem Solving from Nature – PPSN XVIII, page 100--115. Springer, Berlin u. a., (Sep 7, 2024)Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024..
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Hardness,
Traveling
Configuration,
Benchmarking,
Search,
Problem
topic_engineering
Algorithm
Heuristic
Salesperson
FIS_scadsHardness,TravelingConfiguration,Benchmarking,Search,Problemtopic_engineeringAlgorithmHeuristicSalesperson
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1Artificial neural network small-sample-bias-corrections of the AR(1) parameter close to unit root
H. Jiang, O. Okhrin, and M. Rockinger. Statistica Neerlandica, (Jul 31, 2024)Publisher Copyright: © 2024 The Author(s). Statistica Neerlandica published by John Wiley & Sons Ltd on behalf of Netherlands Society for Statistics and Operations Research..
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small
network,
neural
bias
topic_engineering
correction,
sample
FIS_scadssmallnetwork,neuralbiastopic_engineeringcorrection,sample
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1Modelling delamination in fibre-reinforced composites subjected to through-thickness compression by an adapted cohesive law
M. Kuhtz, J. Gerritzen, J. Wiegand, A. Hornig, and M. Gude. 14th European LS-DYNA Users Conference, DYNAmore GmbH, (October 2023)14th European LS-DYNA Conference 2023 ; Conference date: 18-10-2023 Through 19-10-2023.
a month ago by @scadsfct
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1Leader-Follower Identification with Vehicle-Following Calibration for Non-Lane-Based Traffic
{. Kulkarni, {. Chaudhari, {. Srinivasan, {. Chilukuri, M. Treiber, and O. Okhrin. WorkingPaper, (May 17, 2024)
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topic_engineering
physics.soc-ph
FIS_scadstopic_engineeringphysics.soc-ph
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1Towards Autonomous Driving with Small-Scale Cars: A Survey of Recent Development
D. Li, P. Auerbach, and O. Okhrin. WorkingPaper, (Apr 9, 2024)
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cs.RO,
topic_engineering
cs.AI
FIS_scadscs.RO,topic_engineeringcs.AI
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1Vision-Based DRL Autonomous Driving Agent with Sim2Real Transfer
D. Li, and O. Okhrin. 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023, page 866--873. United States of America, Institute of Electrical and Electronics Engineers Inc., (2023)Publisher Copyright: © 2023 IEEE.; 26th IEEE International Conference on Intelligent Transportation Systems : Towards a New Era of Human-aware, Human-interactive, and Human-friendly ITS, ITSC 2023 ; Conference date: 24-09-2023 Through 28-09-2023.
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1Penalized estimation of hierarchical Archimedean copula
O. Okhrin, and A. Ristig. Journal of Multivariate Analysis, (2023)Publisher Copyright: © 2023.
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Hierarchical
estimation
Stage-wise
copula,
estimation,
likelihood
Archimedean
topic_engineering
Maximum
FIS_scadsHierarchicalestimationStage-wisecopula,estimation,likelihoodArchimedeantopic_engineeringMaximum
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1Robust path following on rivers using bootstrapped reinforcement learning
N. Paulig, and O. Okhrin. Ocean engineering, (Apr 15, 2024)Publisher Copyright: © 2024 The Authors.
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FIS_scads
Path
waterways
Autonomous
Restricted
learning,
surface
vessel,
reinforcement
Deep
topic_engineering
following,
FIS_scadsPathwaterwaysAutonomousRestrictedlearning,surfacevessel,reinforcementDeeptopic_engineeringfollowing,
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1An open-source framework for data-driven trajectory extraction from AIS data—The α-method
N. Paulig, and O. Okhrin. Ocean engineering, (Nov 15, 2024)Publisher Copyright: © 2024 The Author(s).
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Trajectory
topic_engineering
Data-driven,
extraction
Big
Open-source,
data,
AIS,
FIS_scadsTrajectorytopic_engineeringData-driven,extractionBigOpen-source,data,AIS,
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publications (hide)27
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1Flight delay propagation inference in air transport networks using the multilayer perceptron

1Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization

2Dynamic tail risk forecasting: what do realized skewness and kurtosis add?

1Copula modeling from Abe Sklar to the present day

1Preface to the Special Issue “Copula modeling from Abe Sklar to the present day”

1Graph based process models as basis for efficient data driven surrogates - Expediting the material development process

1A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP

1Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning

1Developing a numerical modelling strategy for metallic pin pressing processes in fibre reinforced thermoplastics to investigate fibre rearrangement mechanisms during joining

1Vessel-following model for inland waterways based on deep reinforcement learning

1Two-step dynamic obstacle avoidance

1Dancing to the State of the Art?: How Candidate Lists Influence LKH for Solving the Traveling Salesperson Problem

1Artificial neural network small-sample-bias-corrections of the AR(1) parameter close to unit root

1Modelling delamination in fibre-reinforced composites subjected to through-thickness compression by an adapted cohesive law

1Leader-Follower Identification with Vehicle-Following Calibration for Non-Lane-Based Traffic

1Towards Autonomous Driving with Small-Scale Cars: A Survey of Recent Development

1Vision-Based DRL Autonomous Driving Agent with Sim2Real Transfer

1Penalized estimation of hierarchical Archimedean copula

1Robust path following on rivers using bootstrapped reinforcement learning

1An open-source framework for data-driven trajectory extraction from AIS data—The α-method

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