Publications
Digital Engineering (Design Automation, Optimization & Machine Learning Application)
L. Bonal, Z. Nasrollahinayeri, M. Hamilton-Jones, K. Dimovski, D. Entner, P. Ohnewein, H. Trinkl (2024)
Predict-It: Forecasting District Heating Loads with an Open-Source and User-Friendly Neural Network-Powered Platform
Accepted for the International Sustainable Energy Conference (ISEC) 2024.
K. Dimovski, L. Bonal, T. Zengerle, U. Hüttinger, N. Linder, D. Entner (2023)
Condition Monitoring and Anomaly Detection: Real-world Challenges and Successes
In Data Science—Analytics and Applications. iDSC 2023. Springer, Cham. https://doi.org/10.1007/978-3-031-42171-6_12
S. Bitrus, H. Fitzek, E. Rigger, J. Rattenberger, D. Entner (2022)
Enhancing classification in correlative microscopy using multiple classifier systems with dynamic selection
Ultramicroscopy 240(2022): 113567. https://doi.org/10.1016/j.ultramic.2022.113567
D. Entner, P. Fleck, T. Vosgien, C. Münzer, S. Finck, T. Prante, M. Schwarz (2019)
A Systematic Approach for the Selection of Optimization Algorithms including End-User Requirements Applied to Box-Type Boom Crane Design
Applied System Innovation, 2(3), 20. https://doi.org/10.3390/asi2030020
D. Entner, T. Prante, T. Vosgien, C. Zavoianu, S. Saminger-Platz, M. Schwarz, K. Fink (2019)
Potential identification and industrial evaluation of an integrated design automation workflow
Journal of Engineering Design and Technology, Vol. 17 No. 6, pp. 1085-1109. https://doi.org/10.1108/JEDT-06-2018-0096
M. Hellwig, D. Entner, T. Prante, C. Zavoianu, M. Schwarz, K. Fink. (2019)
Optimization of Ascent Assembly Design Based on a Combinatorial Problem Representation
In book: Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. Computational Methods in Applied Sciences, vol 49. Springer, Cham. (EUROGEN 2017, Madrid, Spain). https://doi.org/10.1007/978-3-319-89890-2_19
C. Zavoianu, S. Saminger-Platz, D. Entner, T. Prante, M. Hellwig, M. Schwarz, K. Fink (2019)
On the Optimization of 2D Path Network Layouts in Engineering Designs via Evolutionary Computation Techniques
In book: Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. Computational Methods in Applied Sciences, vol 49. Springer, Cham. (EUROGEN 2017, Madrid, Spain). https://doi.org/10.1007/978-3-319-89890-2_20
R. Fleisch, D. Entner, T. Prante, R. Pfefferkorn (2019)
Interactive Optimization of Path Planning for a Robot Enabled by Virtual Commissioning
In book: Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. Computational Methods in Applied Sciences, vol 49. Springer, Cham. (EUROGEN 2017, Madrid, Spain). https://doi.org/10.1007/978-3-319-89890-2_22
P. Fleck, D. Entner, C. Münzer, M. Kommenda, T. Prante, M. Affenzeller, M. Schwarz, M. Hächl (2019)
Box-Type Boom Design Using Surrogate Modeling: Introducing an Industrial Optimization Benchmark
In book: Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. Computational Methods in Applied Sciences, vol 49. Springer, Cham. (EUROGEN 2017, Madrid, Spain). https://doi.org/10.1007/978-3-319-89890-2_23
C. Zavoianu, S. Saminger-Platz, D. Entner, T. Prante, M. Hellwig, M. Schwarz, K. Fink (2018)
Multi-Objective Optimal Design of Obstacle-Avoiding Two-Dimensional Steiner Trees With Application to Ascent Assembly Engineering
Journal of Mechanical Design 140(6): 061404. https://doi.org/10.1115/1.4039009
G. Frank, D. Entner, T. Prante, V. Khachatouri, M. Scharzw (2014)
Towards a Generic Framework of Engineering Design Automation for Creating Complex CAD Models.
International Journal on Advances in Systems and Measurements, Volume 7, Number 1 & 2, pages 179-192
Causal Discovery
D. Entner (2013)
Causal Structure Learning and Effect Identification in Linear Non-Gaussian Models and Beyond
PhD Thesis, Series of Publications A, Report A-2013-10, Department of Computer Science, University of Helsinki
(A collections of methods for causal discovery from passive observational data in various settings. The thesis is based on six articles, and consists of an intro summarizing the related work and the main contributions of these articles.)
A. Moneta, D. Entner, P.O. Hoyer, and A. Coad (2013)
Causal Inference by Independent Component Analysis: Theory and Applications
[link to article on OBES webpage] [link to code package]
Oxford Bulletin of Economics and Statistics, Volume 75, Issue 5, pages 705-730. https://doi.org/10.1111/j.1468-0084.2012.00710.x
(Introduction of a method, termed VAR-LiNGAM, for SVAR identification in linear non-Gaussian models using ICA to the econometrics community and demonstration of this method by application to economic data.)
D. Entner, P.O. Hoyer, and P. Spirtes (2013)
Data-driven covariate selection for nonparametric estimation of causal effects
[pdf - article] [pdf - supp. material] [link to code package]
Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS-2013), Scottsdale, Arizona. Proceedings of Machine Learning Research 31:256-264.
(Introduction of two simple rules based on statistical dependencies and independencies to select a set of covariates for adjustment to obtain a consistent estimator of a causal effect from passive observational data.)
D. Entner, and P.O. Hoyer (2012)
Estimating a Causal Order among Groups of Variables in Linear Models
[pdf - article] [pdf - online appendix] [link to code package]
In: Artificial Neural Networks and Machine Learning – ICANN 2012 (Lausanne, Switzerland). Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_11
Presented at the UAI Workshop on Causal Structure Learning 2012, Catalina Island, California
(Introduction of a set of methods to infer a causal order among multi-dimensional vectors of random variables in linear models.)
D. Entner, P.O. Hoyer, and P. Spirtes (2012)
Statistical test for consistent estimation of causal effects in linear non-Gaussian models
[pdf - article] [pdf - supp. material] [link to code package]
Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-2012), La Palma, Canary Islands. Proceedings of Machine Learning Research 22:364-372.
(Introduction of a statistical test for linear non-Gaussian acyclic models with hidden variables to infer from passive observational data whether an estimator for a causal effect is consistent.)
D. Entner, and P.O. Hoyer (2011)
Discovering Unconfounded Causal Relationships Using Linear Non-Gaussian Models
[pdf - article] [link to code package]
New Frontiers in Artificial Intelligence: JSAI-isAI 2010 Workshops, Tokyo, Japan. Lecture Notes in Computer Science, vol 6797. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25655-4_17
(Introduction of an algorithm to discover pairwise unconfounded total effects from data generated by a linear non-Gaussian acyclic model allowing for hidden variables.)
A. Moneta, N. Chlass, D. Entner, and P.O. Hoyer (2011)
Causal Search in Structural Vector Autoregressive Models
Proceedings of the Neural Information Processing Systems (NIPS) Mini-Symposium on Causality in Time Series. Proceedings of Machine Learning Research 12:95-114.
(Review of a class of methods for causal inference in SVAR models.)
D. Entner, and P.O. Hoyer (2010)
On causal discovery from time series data using FCI
[pdf - article] [link to code package]
Proceedings of the 5th European Workshop on Probabilistic Graphical Models (PGM-2010), Helsinki, Finland (pp. 121-128)
(Adaptation of the FCI algorithm of Spirtes et al. (2000) to learn causal relationships from time series data in the presence of confounding variables.)