Nils Thuerey
Professor
Develops deep learning methods for PDE-based physics simulations, including differentiable physics solvers for fluids.
Nils Thuerey's lab at TUM works on physics, climate & scientific computing and physics-based simulation & animation. 41 papers since 2023, 28 at top venues, mostly ICLR and NeurIPS. 11 PhD students. 20 alumni.
At a glance
- papers, 2023–2026
- 11/ year
- at T1 venues
- 68%
- PhD students
- 11
- MS students
- 0
- alumni listed
- 20
Papers by yearfrom the lab homepage, 2023–2026
- 15 papers2026 · T1 12 · collecting
- 12 papers2025 · T1 10
- 9 papers2024 · T1 3
- 5 papers2023 · T1 3
T1 venuesOtherVenues: ICLR, NeurIPS, ICML, TOG, AAAI
Research focuspaper titles that use each topic's words
- Physics-based simulation & animation16 papers
- Physics, climate & scientific computing12 papers
- physics11 papers
- simulations6 papers
- solvers3 papers
Paper list
From the lab homepage.
202615 papers
- PNAS (T1)A neural operator framework for data-driven discovery of stability and receptivity in physical systems (opens in a new tab)
- NeurIPS (T1)Compute-optimal data scaling for neural surrogates via multi-fidelity training
- NeurIPS (T1)CRAFT: Conflict-Resolved Aggregation for Federated Training
- JCPdiffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning (opens in a new tab)
- ICLR (T1)Flow Matching Meets PDEs – A Unified Framework for Physics-Constrained Generation (opens in a new tab)
- NeurIPS (T1)Geometry Meets Physics: Data-Efficient Pre-Training for Unstructured Neural PDE Solvers
- PoFGuiding diffusion models to reconstruct flow fields from sparse data (opens in a new tab)
- ICML (T1)On the Generalization in Topology Optimization via Sensitivity-Conditioned Bernoulli Flow Matching (opens in a new tab)
- ICLR (T1)P3D: Scalable Neural Surrogates for High-Resolution 3D Physics Simulations with Global Context (opens in a new tab)
- ICML (T1)Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control (opens in a new tab)
- ICML (T1)ReViT: Rotational-equivariant Vision Transformers for Neural PDE Solvers (opens in a new tab)
- NeurIPS (T1)Solver-as-Teacher: Solver-Guided On-Policy Post-Training Framework for PDE Foundation Models
202512 papers
- TOG (T1)Adaptive Phase-Field-FLIP for Very Large Scale Two-Phase Fluid Simulation (opens in a new tab)
- ICLR (T1)ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks (opens in a new tab)
- NeurIPS (T1)INC: An Indirect Neural Corrector for Auto-Regressive Hybrid PDE Solvers (opens in a new tab)
- ICLR (T1)Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks (opens in a new tab)
- CVPR (T1)Light Transport-Aware Diffusion Posterior Sampling for Single-View Reconstruction of 3D Volumes (opens in a new tab)
- NeurIPS (T1)Neural Emulator Superiority: When Machine Learning for PDEs Surpasses its Training Data (opens in a new tab)
- ICML (T1)PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations (opens in a new tab)
- JCPPICT – A Differentiable, GPU-Accelerated Multi-Block PISO Solver for Simulation-Coupled Learning Tasks in Fluid Dynamics (opens in a new tab)
- ICLR (T1)Progressively Refined Differentiable Physics (opens in a new tab)
- PoFRotational Equivariant Graph Neural Networks via local Eigenbasis Transformations (opens in a new tab)
- ICLR (T1)Temporal Difference Learning: Why It Can Be Fast and How It Will Be Faster (opens in a new tab)
- ICLR (T1)Truncation Is All You Need: Improved Sampling Of Diffusion Models For Physics-Based Simulations (opens in a new tab)
20249 papers
- NeurIPS (T1)APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs (opens in a new tab)
- Neural Networks JournalBenchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation (opens in a new tab)
- PoFDeep learning-based predictive modelling of transonic flow over an aerofoil (opens in a new tab)
- CMAMEHow Temporal Unrolling Supports Neural Physics Simulators (opens in a new tab)
- JOSSPhi-ML: Intuitive Scientific Computing with Dimension Types for Jax, PyTorch, TensorFlow and NumPy (opens in a new tab)
- ICLR (T1)Stabilizing Backpropagation Through Time to Learn Complex Physics (opens in a new tab)
- ICLR (T1)Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics (opens in a new tab)
- AIAAUncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models (opens in a new tab)
- PREUnsteady Cylinder Wakes from Arbitrary Bodies with Differentiable Physics-Assisted Neural Network (opens in a new tab)
20235 papers
- DCEIncomplete to complete multiphysics forecasting – a hybrid approach for learning unknown phenomena (opens in a new tab)
- AAAI (T1)Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs (opens in a new tab)
- ICLR (T1)Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision (opens in a new tab)
- NeurIPS (T1)Solving Inverse Physics Problems with Score Matching (opens in a new tab)
- CaFTowards high-accuracy deep learning inference of compressible flows over aerofoils (opens in a new tab)
Where alumni went20 listed
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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