Scholar
Samuel Lanthaler
Google Scholar ID: v-Jv3LoAAAAJ
University of Vienna
Citations & Impact
Citations
1,589
H-index
16
i10-index
19
Publications
20
Co-authors
11
list available
Publications
Resume (English only)
Academic Achievements
- Publications:
- - Theory-to-Practice Gap for Neural Networks and Neural Operators
- - Generative AI for fast and accurate statistical computation of fluids
- - Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective
- - Data-Complexity Estimates for Operator Learning
- - Discretization Error of Fourier Neural Operators
- - Sharp conditions for energy balance for the two-dimensional incompressible Euler equations with external force
- - Operator Learning: Algorithms and Analysis
- Conference Presentations: SIAM UQ24, Trieste, Italy, Topic: Operator Learning in Uncertainty Quantification
Research Experience
- Position: Assistant Professor (Mathematics of Data Science); Institution: University of Vienna; Previously a postdoctoral researcher and visiting scholar at Caltech, hosted by Andrew Stuart
Education
- PhD in Mathematics; University: Not explicitly mentioned, but supervised by Siddhartha Mishra; Time: Not explicitly mentioned
Background
- Research Interests: Theoretical underpinnings and algorithmic innovation in deep learning for scientific computing; Professional Field: Mathematics of Data Science; Summary: Aims to understand how novel deep learning approaches can effectively complement traditional numerical methods, and to develop new methodologies that enhance the scalability and modeling accuracy of current methods.
Miscellany
- Personal interests and hobbies are not provided
