Magnus Fast

755 total citations
11 papers, 584 citations indexed

About

Magnus Fast is a scholar working on Control and Systems Engineering, Artificial Intelligence and Mechanics of Materials. According to data from OpenAlex, Magnus Fast has authored 11 papers receiving a total of 584 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Control and Systems Engineering, 6 papers in Artificial Intelligence and 4 papers in Mechanics of Materials. Recurrent topics in Magnus Fast's work include Neural Networks and Applications (6 papers), Fault Detection and Control Systems (5 papers) and Technical Engine Diagnostics and Monitoring (3 papers). Magnus Fast is often cited by papers focused on Neural Networks and Applications (6 papers), Fault Detection and Control Systems (5 papers) and Technical Engine Diagnostics and Monitoring (3 papers). Magnus Fast collaborates with scholars based in Sweden, Norway and India. Magnus Fast's co-authors include Mohsen Assadi, S. K. De, Thomas Palmé, Jaka Smrekar, Marcus Thern, Magnus Genrup and Peter Breuhaus and has published in prestigious journals such as Applied Energy, Energy and Neural Computing and Applications.

In The Last Decade

Magnus Fast

11 papers receiving 551 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Magnus Fast	286	172	145	100	96	11	584
Shilie Weng	166 0.6×	301 1.8×	281 1.9×	146 1.5×	29 0.3×	85	899
Yuguang Niu	134 0.5×	135 0.8×	109 0.8×	94 0.9×	24 0.3×	48	478
Mohammadreza Tahan	237 0.8×	102 0.6×	84 0.6×	69 0.7×	39 0.4×	8	479
Tingting Yang	319 1.1×	169 1.0×	171 1.2×	95 0.9×	141 1.5×	44	752
Mingliang Bai	230 0.8×	75 0.4×	201 1.4×	27 0.3×	150 1.6×	31	519
Sergey Ushakov	370 1.3×	155 0.9×	49 0.3×	115 1.1×	61 0.6×	16	779
Yujiong Gu	136 0.5×	207 1.2×	290 2.0×	64 0.6×	27 0.3×	52	631
Ka In Wong	120 0.4×	122 0.7×	110 0.8×	141 1.4×	177 1.8×	21	576
Liang Lu	165 0.6×	268 1.6×	113 0.8×	112 1.1×	17 0.2×	47	639
Yongxiang Zhang	358 1.3×	305 1.8×	72 0.5×	62 0.6×	26 0.3×	69	660

Countries citing papers authored by Magnus Fast

Since Specialization

Citations

This map shows the geographic impact of Magnus Fast's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Magnus Fast with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Magnus Fast more than expected).

Fields of papers citing papers by Magnus Fast

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Magnus Fast. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Magnus Fast. The network helps show where Magnus Fast may publish in the future.

Co-authorship network of co-authors of Magnus Fast

This figure shows the co-authorship network connecting the top 25 collaborators of Magnus Fast. A scholar is included among the top collaborators of Magnus Fast based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Magnus Fast. Magnus Fast is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

Palmé, Thomas, Magnus Fast, & Marcus Thern. (2011). Gas turbine sensor validation through classification with artificial neural networks. Applied Energy. 88(11). 3898–3904. 67 indexed citations

Fast, Magnus, Thomas Palmé, & Magnus Genrup. (2009). A Novel Approach for Gas Turbine Condition Monitoring Combining CUSUM Technique and Artificial Neural Network. Lund University Publications (Lund University). 567–574. 9 indexed citations

Smrekar, Jaka, et al.. (2009). Prediction of power output of a coal-fired power plant by artificial neural network. Neural Computing and Applications. 19(5). 725–740. 57 indexed citations

Palmé, Thomas, et al.. (2009). Different Condition Monitoring Models for Gas Turbines by Means of Artificial Neural Networks. 543–553. 6 indexed citations

Fast, Magnus & Thomas Palmé. (2009). Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant. Energy. 35(2). 1114–1120. 105 indexed citations

Fast, Magnus, Mohsen Assadi, & S. K. De. (2008). Development and multi-utility of an ANN model for an industrial gas turbine. Applied Energy. 86(1). 9–17. 118 indexed citations

Fast, Magnus, Mohsen Assadi, & S. K. De. (2008). Application of artificial neural network to the condition monitoring and diagnosis of a CHP plant. Lund University Publications (Lund University). 981–988. 3 indexed citations

Fast, Magnus, Mohsen Assadi, & S. K. De. (2008). Condition Based Maintenance of Gas Turbines Using Simulation Data and Artificial Neural Network: A Demonstration of Feasibility. 153–161. 24 indexed citations

Smrekar, Jaka, et al.. (2008). Development of artificial neural network model for a coal-fired boiler using real plant data. Energy. 34(2). 144–152. 121 indexed citations

10.

De, S. K., et al.. (2007). Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden. Energy. 32(11). 2099–2109. 73 indexed citations

11.

Fast, Magnus. (2006). Artificial Neural Networks for Gas Turbine Modeling and Sensor Validation. Lund University Publications Student Papers (Lund University). 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact