Felix Dietrich
Professor
Develops numerical algorithms for machine learning, including manifold learning, sampled neural networks and their links to Gaussian processes for modelling dynamical systems.
Felix Dietrich's lab at TUM works on probabilistic & causal inference. 18 papers since 2023, 1 at top venues, mostly TMLR. 16 PhD students.
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 6%
- PhD students
- 16
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 2 papers2026 · T1 1 · collecting
- 5 papers2025 · T1 0
- 7 papers2024 · T1 0
- 4 papers2023 · T1 0
T1 venuesOtherVenues: TMLR
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference1 paper
- develops numerical0 papers
- manifold learning0 papers
- links0 papers
- manifold0 papers
Paper list
From the lab homepage.
20262 papers
- Computational Science - ICCSEmbedding-Based Methods for Linear Solver Performance Prediction
- TMLR (T1)Koopman-informed recurrent neural networks
20255 papers
- Computational MechanicsAccelerating full waveform inversion by transfer learning
- TBANeural network-based singularity detection and applications
- TBARandom Feature Spiking Neural Networks
- TBARapid training of Hamiltonian graph networks without gradient descent
- Mathematische SemesterberichteScientific machine learning
20247 papers
- SIAM Journal on Scientific ComputingA Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms
- Chaos: An Interdisciplinary Journal of Nonlinear ScienceData-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operator
- TBAGradient-free training of recurrent neural networks
- Machine Learning: Science and TechnologyMulti-fidelity Gaussian process surrogate modeling for regression problems in physics
- PNAS NexusOn learning what to learn: Heterogeneous observations of dynamics and establishing possibly causal relations among them
- tbaSolving partial differential equations with sampled neural networks
- Chaos: An Interdisciplinary Journal of Nonlinear ScienceTransformations establishing equivalence across neural networks: When have two networks learned the same task?
20234 papers
- Journal of Computational PhysicsDouble Diffusion Maps and their Latent Harmonics for scientific computations in latent space
- Chaos: An Interdisciplinary Journal of Nonlinear ScienceLearning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning
- Quantum Information ProcessingQuantum process tomography of unitary maps from time-delayed measurements
- Conference on Neural Information Processing Systems (NeurIPSSampling weights of deep neural networks
Where alumni went
Alumni are not listed on the lab homepage.
Apply to TUMRolling
Not announced yet
Computer Science Doctorate (CIT)
- Round
- Rolling (position-based)
- Opens
- —
- GRE
- See program page
- English
- Very good written and spoken English; no minimum test score
- Fee
- See program page
Confirmed on the program page.
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Data
Some numbers could not be verified:
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.