@article{KochBernalNeiraChenetal.2025, author = {Koch, Thorsten and Bernal Neira, David E. and Chen, Ying and Cortiana, Giorgio and Egger, Daniel J. and Heese, Raoul and Hegade, Narendra N. and Gomez Cadavid, Alejandro and Huang, Rhea and Itoko, Toshinari and Kleinert, Thomas and Maciel Xavier, Pedro and Mohseni, Naeimeh and Montanez-Barrera, Jhon A. and Nakano, Koji and Nannicini, Giacomo and O'Meara, Corey and Pauckert, Justin and Proissl, Manuel and Ramesh, Anurag and Schicker, Maximilian and Shimada, Noriaki and Takeori, Mitsuharu and Valls, Victor and Van Bulck, David and Woerner, Stefan and Zoufal, Christa}, title = {Quantum Optimization Benchmark Library -- The Intractable Decathlon}, arxiv = {http://arxiv.org/abs/2504.03832}, year = {2025}, abstract = {Through recent progress in hardware development, quantum computers have advanced to the point where benchmarking of (heuristic) quantum algorithms at scale is within reach. Particularly in combinatorial optimization -- where most algorithms are heuristics -- it is key to empirically analyze their performance on hardware and track progress towards quantum advantage. To this extent, we present ten optimization problem classes that are difficult for existing classical algorithms and can (mostly) be linked to practically-relevant applications, with the goal to enable systematic, fair, and comparable benchmarks for quantum optimization methods. Further, we introduce the Quantum Optimization Benchmark Library (QOBLIB) where the problem instances and solution track records can be found. The individual properties of the problem classes vary in terms of objective and variable type, coefficient ranges, and density. Crucially, they all become challenging for established classical methods already at system sizes ranging from less than 100 to, at most, an order of 100,000 decision variables, allowing to approach them with today's quantum computers. We reference the results from state-of-the-art solvers for instances from all problem classes and demonstrate exemplary baseline results obtained with quantum solvers for selected problems. The baseline results illustrate a standardized form to present benchmarking solutions, which has been designed to ensure comparability of the used methods, reproducibility of the respective results, and trackability of algorithmic and hardware improvements over time. We encourage the optimization community to explore the performance of available classical or quantum algorithms and hardware platforms with the benchmarking problem instances presented in this work toward demonstrating quantum advantage in optimization.}, language = {en} } @article{ZhouChenPengetal.2025, author = {Zhou, Lei and Chen, Ying and Peng, Hanqiu and Koch, Thorsten}, title = {Is innovation slowing down? Insights from the AIMS framework of patent values}, volume = {280}, journal = {Expert Systems with Applications}, doi = {10.1016/j.eswa.2025.127355}, pages = {127355}, year = {2025}, abstract = {Amidst the unprecedented expansion of scientific and technological knowledge over the past century, concerns persist regarding a slowdown in innovation. To address this, we introduce the AIMS framework, which categorizes patents into four types—Aurora, Invisible, Mirage, and Success—based on their respective inherent scientific values and market-recognized economic values. Utilizing USPTO patent and citation data from 1976 to 2022, our analysis reveals an increasing volume of patent issuances but a concerning dilution in scientific quality starting in the 2000s. This trend is primarily attributed to the rise of low scientific value patents—categorized as Mirage and Invisible—and a modest decline in high-impact scientific patents—categorized as Success and Aurora. Meanwhile, the economic value of patents has risen, especially noted with the growth in Mirage patents since the 2010s, indicating a shift towards strategies that prioritize market-driven patenting. This study highlights the evolving nature of patents from mere indicators of scientific innovation to strategic tools for market dominance, providing an alternative understanding of patent value and its implications for firms' strategic decisions over patent issuance across different sectors.}, language = {en} } @article{ChenKochPengetal.2025, author = {Chen, Ying and Koch, Thorsten and Peng, Hanqui and Zhang, Hongrui}, title = {Benchmarking of Quantum and Classical Computing in Large-Scale Dynamic Portfolio Optimization Under Market Frictions}, arxiv = {http://arxiv.org/abs/2502.05226}, year = {2025}, abstract = {Quantum computing is poised to transform the financial industry, yet its advantages over traditional methods have not been evidenced. As this technology rapidly evolves, benchmarking is essential to fairly evaluate and compare different computational strategies. This study presents a challenging yet solvable problem of large-scale dynamic portfolio optimization under realistic market conditions with frictions. We frame this issue as a Quadratic Unconstrained Binary Optimization (QUBO) problem, compatible with digital computing and ready for quantum computing, to establish a reliable benchmark. By applying the latest solvers to real data, we release benchmarks that help verify true advancements in dynamic trading strategies, either quantum or digital computing, ensuring that reported improvements in portfolio optimization are based on robust, transparent, and comparable metrics.}, language = {en} } @article{XuChenZhangetal.2022, author = {Xu, Xiaofei and Chen, Ying and Zhang, Ge and Koch, Thorsten}, title = {Modeling Functional Time Series and Mixed-Type Predictors With Partially Functional Autoregressions}, volume = {42}, journal = {Journal of Business \& Economic Statistics}, number = {2}, publisher = {Informa UK Limited}, issn = {0735-0015}, doi = {https://doi.org/10.1080/07350015.2021.2011299}, pages = {349 -- 366}, year = {2022}, language = {en} } @article{PetkovicChenGamrathetal.2022, author = {Petkovic, Milena and Chen, Ying and Gamrath, Inken and Gotzes, Uwe and Hadjidimitrou, Natalia Selini and Zittel, Janina and Xu, Xiaofei and Koch, Thorsten}, title = {A hybrid approach for high precision prediction of gas flows}, volume = {13}, journal = {Energy Systems}, doi = {10.1007/s12667-021-00466-4}, pages = {383 -- 408}, year = {2022}, abstract = {About 23\% of the German energy demand is supplied by natural gas. Additionally, for about the same amount Germany serves as a transit country. Thereby, the German network represents a central hub in the European natural gas transport network. The transport infrastructure is operated by transmissions system operators (TSOs). The number one priority of the TSOs is to ensure the security of supply. However, the TSOs have only very limited knowledge about the intentions and planned actions of the shippers (traders). Open Grid Europe (OGE), one of Germany's largest TSO, operates a high-pressure transport network of about 12,000 km length. With the introduction of peak-load gas power stations, it is of great importance to predict in- and out-flow of the network to ensure the necessary flexibility and security of supply for the German Energy Transition ("Energiewende"). In this paper, we introduce a novel hybrid forecast method applied to gas flows at the boundary nodes of a transport network. This method employs an optimized feature selection and minimization. We use a combination of a FAR, LSTM and mathematical programming to achieve robust high-quality forecasts on real-world data for different types of network nodes.}, language = {en} } @misc{PetkovicChenGamrathetal.2019, author = {Petkovic, Milena and Chen, Ying and Gamrath, Inken and Gotzes, Uwe and Hadjidimitriou, Natalia Selini and Zittel, Janina and Xu, Xiaofei and Koch, Thorsten}, title = {A Hybrid Approach for High Precision Prediction of Gas Flows}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-73525}, year = {2019}, abstract = {About 20\% of the German energy demand is supplied by natural gas. Ad- ditionally, for about twice the amount Germany serves as a transit country. Thereby, the German network represents a central hub in the European natural gas transport network. The transport infrastructure is operated by so-called transmissions system operators or TSOs. The number one priority of the TSOs is to ensure security of supply. However, the TSOs have no knowledge of the intentions and planned actions of the shippers (traders). Open Grid Europe (OGE), one of Germany's largest TSO, operates a high- pressure transport network of about 12.000 km length. Since flexibility and security of supply is of utmost importance to the German Energy Transition ("Energiewende") especially with the introduction of peak-load gas power stations, being able to predict in- and out-flow of the network is of great importance. In this paper we introduce a new hybrid forecast method applied to gas flows at the boundary nodes of a transport network. The new method employs optimized feature minimization and selection. We use a combination of an FAR, LSTM DNN and mathematical programming to achieve robust high quality forecasts on real world data for different types of network nodes. Keywords: Gas Forecast, Time series, Hybrid Method, FAR, LSTM, Mathematical Optimisation}, language = {en} } @article{XuChenZhangetal.2021, author = {Xu, Xiaofei and Chen, Ying and Zhang, Ge and Koch, Thorsten}, title = {Modeling functional time series and mixed-type predictors with partially functional autoregressions*}, journal = {Journal of Business and Economic Statistics}, doi = {10.1080/07350015.2021.2011299}, pages = {1 -- 43}, year = {2021}, abstract = {In many business and economics studies, researchers have sought to measure the dynamic dependence of curves with high-dimensional mixed-type predictors. We propose a partially functional autoregressive model (pFAR) where the serial dependence of curves is controlled by coefficient operators that are defined on a two-dimensional surface, and the individual and group effects of mixed-type predictors are estimated with a two-layer regularization. We develop an efficient estimation with the proven asymptotic properties of consistency and sparsity. We show how to choose the sieve and tuning parameters in regularization based on a forward-looking criterion. In addition to the asymptotic properties, numerical validation suggests that the dependence structure is accurately detected. The implementation of the pFAR within a real-world analysis of dependence in German daily natural gas flow curves, with seven lagged curves and 85 scalar predictors, produces superior forecast accuracy and an insightful understanding of the dynamics of natural gas supply and demand for the municipal, industry, and border nodes, respectively.}, language = {en} } @article{ChenKochZakiyevaetal.2023, author = {Chen, Ying and Koch, Thorsten and Zakiyeva, Nazgul and Liu, Kailiang and Xu, Zhitong and Chen, Chun-houh and Nakano, Junji and Honda, Keisuke}, title = {Article's scientific prestige: Measuring the impact of individual articles in the web of science}, volume = {17}, journal = {Journal of Informetrics}, number = {1}, doi = {10.1016/j.joi.2023.101379}, pages = {101379}, year = {2023}, abstract = {We performed a citation analysis on the Web of Science publications consisting of more than 63 million articles and 1.45 billion citations on 254 subjects from 1981 to 2020. We proposed the Article's Scientific Prestige (ASP) metric and compared this metric to number of citations (\#Cit) and journal grade in measuring the scientific impact of individual articles in the large-scale hierarchical and multi-disciplined citation network. In contrast to \#Cit, ASP, that is computed based on the eigenvector centrality, considers both direct and indirect citations, and provides steady-state evaluation cross different disciplines. We found that ASP and \#Cit are not aligned for most articles, with a growing mismatch amongst the less cited articles. While both metrics are reliable for evaluating the prestige of articles such as Nobel Prize winning articles, ASP tends to provide more persuasive rankings than \#Cit when the articles are not highly cited. The journal grade, that is eventually determined by a few highly cited articles, is unable to properly reflect the scientific impact of individual articles. The number of references and coauthors are less relevant to scientific impact, but subjects do make a difference.}, language = {en} } @article{RongChenMaetal.2025, author = {Rong, Guoyang and Chen, Ying and Ma, Feicheng and Koch, Thorsten}, title = {Exploring Interdisciplinary Research Trends through Critical Years for Interdisciplinary Citation}, volume = {19}, journal = {Journal of Informetrics}, number = {4}, doi = {10.1016/j.joi.2025.101726}, year = {2025}, language = {en} } @article{ChenKochPengetal.2025, author = {Chen, Ying and Koch, Thorsten and Peng, Hanqui and Zhang, Hongrui}, title = {Benchmarking of Quantum and Classical Computing in Large-Scale Dynamic Portfolio Optimization Under Market Frictions}, arxiv = {http://arxiv.org/abs/2502.05226}, year = {2025}, language = {en} } @article{ChenXuKoch2020, author = {Chen, Ying and Xu, Xiuqin and Koch, Thorsten}, title = {Day-ahead high-resolution forecasting of natural gas demand and supply in Germany with a hybrid model}, volume = {262}, journal = {Applied Energy}, number = {114486}, doi = {https://doi.org/10.1016/j.apenergy.2019.114486}, year = {2020}, abstract = {As the natural gas market is moving towards short-term planning, accurate and robust short-term forecasts of the demand and supply of natural gas is of fundamental importance for a stable energy supply, a natural gas control schedule, and transport operation on a daily basis. We propose a hybrid forecast model, Functional AutoRegressive and Convolutional Neural Network model, based on state-of-the-art statistical modeling and artificial neural networks. We conduct short-term forecasting of the hourly natural gas flows of 92 distribution nodes in the German high-pressure gas pipeline network, showing that the proposed model provides nice and stable accuracy for different types of nodes. It outperforms all the alternative models, with an improved relative accuracy up to twofold for plant nodes and up to fourfold for municipal nodes. For the border nodes with rather flat gas flows, it has an accuracy that is comparable to the best performing alternative model.}, language = {en} } @article{ChenZakiyevaZhuetal.2020, author = {Chen, Ying and Zakiyeva, Nazgul and Zhu, Bangzhu and Koch, Thorsten}, title = {Modeling and Forecasting the Dynamics of the Natural Gas Transmission Network in Germany with the Demand and Supply Balance Constraint}, journal = {Applied Energy}, number = {278}, doi = {10.1016/j.apenergy.2020.115597}, year = {2020}, language = {en} } @article{KochChenLimetal.2020, author = {Koch, Thorsten and Chen, Ying and Lim, Kian Guan and Xu, Xiaofei and Zakiyeva, Nazgul}, title = {A review study of functional autoregressive models with application to energy forecasting}, journal = {WIREs Computational Statistics}, doi = {10.1002/wics.1525}, year = {2020}, language = {en} } @article{ChenKochZakiyevaetal.2022, author = {Chen, Ying and Koch, Thorsten and Zakiyeva, Nazgul and Liu, Kailiang and Xu, Zhitong and Chen, Chun-houh and Nakano, Junji and Honda, Keisuke}, title = {Article's Scientific Prestige: Measuring the Impact of Individual Articles in the Web of Science}, volume = {17}, number = {1}, doi = {https://doi.org/10.1016/j.joi.2023.101379}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-86380}, pages = {101379}, year = {2022}, abstract = {We performed a citation analysis on the Web of Science publications consisting of more than 63 million articles and 1.45 billion citations on 254 subjects from 1981 to 2020. We proposed the Article's Scientific Prestige (ASP) metric and compared this metric to number of citations (\#Cit) and journal grade in measuring the scientific impact of individual articles in the large-scale hierarchical and multi-disciplined citation network. In contrast to \#Cit, ASP, that is computed based on the eigenvector centrality, considers both direct and indirect citations, and provides steady-state evaluation cross different disciplines. We found that ASP and \#Cit are not aligned for most articles, with a growing mismatch amongst the less cited articles. While both metrics are reliable for evaluating the prestige of articles such as Nobel Prize winning articles, ASP tends to provide more persuasive rankings than \#Cit when the articles are not highly cited. The journal grade, that is eventually determined by a few highly cited articles, is unable to properly reflect the scientific impact of individual articles. The number of references and coauthors are less relevant to scientific impact, but subjects do make a difference.}, language = {en} }