Visualization, Data visualization, Computer graphics, Theoretical computer science and Data mining are his primary areas of study. His Visualization research is under the purview of Artificial intelligence. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Natural language processing.
Peer-Timo Bremer combines subjects such as Data modeling, Visual analytics, Scientific visualization and Distributed computing with his study of Data visualization. His Computer graphics research includes themes of Algorithm, Virtual reality and Reeb graph. His Theoretical computer science study integrates concerns from other disciplines, such as Statistical classification, Graph theory, Polygon mesh and Data structure.
His scientific interests lie mostly in Visualization, Artificial intelligence, Data mining, Algorithm and Data visualization. His research in Visualization intersects with topics in Scalability, Theoretical computer science, Data science and Topology. Peer-Timo Bremer has included themes like Scalar field and Scalar in his Topology study.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Computer vision and Pattern recognition. His research integrates issues of Feature extraction and Cluster analysis in his study of Data mining. The various areas that Peer-Timo Bremer examines in his Algorithm study include Sampling and Vector field.
Peer-Timo Bremer focuses on Artificial intelligence, Machine learning, Visualization, Artificial neural network and Scalability. Peer-Timo Bremer interconnects Sample and Pattern recognition in the investigation of issues within Artificial intelligence. His study on Uncertainty quantification is often connected to Context as part of broader study in Machine learning.
His study in Visualization focuses on Data visualization in particular. The Artificial neural network study combines topics in areas such as Simple and Surrogate model. The study incorporates disciplines such as Supercomputer, Parallel computing, Theoretical computer science, Use case and Topology in addition to Scalability.
Peer-Timo Bremer mainly investigates Visualization, Artificial neural network, Artificial intelligence, Algorithm and Computation. His work on Data visualization as part of general Visualization study is frequently connected to Approximation error, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Artificial neural network study combines topics in areas such as Test data and Data science.
He works mostly in the field of Artificial intelligence, limiting it down to concerns involving Machine learning and, occasionally, Point estimation. His Algorithm research incorporates themes from Sampling artifacts, Inverse and Manifold. In his study, Scalability is inextricably linked to Molecular graph, which falls within the broad field of Computation.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Spectral surface quadrangulation
Shen Dong;Peer-Timo Bremer;Michael Garland;Valerio Pascucci.
international conference on computer graphics and interactive techniques (2006)
Robust on-line computation of Reeb graphs: simplicity and speed
Valerio Pascucci;Giorgio Scorzelli;Peer-Timo Bremer;Ajith Mascarenhas.
international conference on computer graphics and interactive techniques (2007)
Visualizing High-Dimensional Data: Advances in the Past Decade
Shusen Liu;Dan Maljovec;Bei Wang;Peer-Timo Bremer.
IEEE Transactions on Visualization and Computer Graphics (2017)
A topological hierarchy for functions on triangulated surfaces
P.-T. Bremer;B. Hamann;H. Edelsbrunner;V. Pascucci.
IEEE Transactions on Visualization and Computer Graphics (2004)
Combining in-situ and in-transit processing to enable extreme-scale scientific analysis
Janine C. Bennett;Hasan Abbasi;Peer-Timo Bremer;Ray Grout.
ieee international conference on high performance computing data and analytics (2012)
Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data
K. Potter;A. Wilson;P.-T. Bremer;D. Williams.
international conference on data mining (2009)
A Practical Approach to Morse-Smale Complex Computation: Scalability and Generality
A. Gyulassy;P.-T. Bremer;B. Hamann;V. Pascucci.
IEEE Transactions on Visualization and Computer Graphics (2008)
The Helmholtz-Hodge Decomposition—A Survey
H. Bhatia;G. Norgard;V. Pascucci;Peer-Timo Bremer.
IEEE Transactions on Visualization and Computer Graphics (2013)
Understanding the Structure of the Turbulent Mixing Layer in Hydrodynamic Instabilities
D. Laney;P.-T. Bremer;A. Mascarenhas;P. Miller.
IEEE Transactions on Visualization and Computer Graphics (2006)
Visual Exploration of High Dimensional Scalar Functions
S Gerber;P Bremer;V Pascucci;R Whitaker.
IEEE Transactions on Visualization and Computer Graphics (2010)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: