Scholar
Philipp Scholl
Google Scholar ID: UgxPGnUAAAAJ
PhD Student Mathematics, Ludwig-Maximilians University
Artificial Intelligence
Machine Learning
Reinforcement Learning
Symbolic Regression
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Homepage
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Citations & Impact
All-time
Citations
57
H-index
5
i10-index
2
Publications
14
Co-authors
8
list available
Contact
Email
scholl@math.lmu.de
Publications
8 items
Symbolic recovery of PDEs from measurement data
2026
Cited
0
When is a System Discoverable from Data? Discovery Requires Chaos
2025
Cited
0
Improved probabilistic regression using diffusion models
2025
Cited
0
Interpretable Robotic Friction Learning via Symbolic Regression
2025
Cited
0
Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall
2025
Cited
0
Probabilistic neural operators for functional uncertainty quantification
2025
Cited
0
Learning-based adaption of robotic friction models
Robotics Comput. Integr. Manuf. · 2023
Cited
4
ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization
2023
Cited
3
Resume (English only)
Academic Achievements
No particular academic achievements mentioned.
Research Experience
PhD candidate working in the field of Mathematical Foundations of Artificial Intelligence at the Department of Mathematics, LMU Munich.
Education
Details about educational background not specified.
Background
No specific personal background provided.
Miscellany
No additional personal information given.
Co-authors
8 total
Gitta Kutyniok
Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence, LMU Munich
Co-author 2
Christopher Bülte
PhD Student, Ludwig-Maximilians-Universität München
Felix Dietrich
Professor for Physics-Enhanced Machine Learning, Technical University of Munich
Steffen Udluft
Learning Systems, Siemens
Holger Boche
Technische Universität München
Maged Iskandar
German Aerospace Center (DLR)
Sohir Maskey
AI Researcher, Aleph Alpha Research
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