Deep learning based pulse shape discrimination for germanium detectors. - Philipp Holl; L. Hauertmann; Béla Majorovits; Oliver Schulz; Maria Schuster; A. J. Zsigmond (CoRR, 2019)

Learning to Control PDEs with Differentiable Physics. - Philipp Holl; Nils Thuerey; Vladlen Koltun (ICLR, 2020)

Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers. - Kiwon Um; Robert Brand; Yun (Raymond) Fei; Philipp Holl; Nils Thuerey (NeurIPS, 2020)

Physics-based Deep Learning. - Nils Thuerey; Philipp Holl; Maximilian Müller; Patrick Schnell; Felix Trost; Kiwon Um (CoRR, 2021)

Physical Gradients for Deep Learning. - Philipp Holl; Vladlen Koltun; Nils Thuerey (CoRR, 2021)

Half-Inverse Gradients for Physical Deep Learning. - Patrick Schnell; Philipp Holl; Nils Thuerey (ICLR, 2022)

Scale-invariant Learning by Physics Inversion. - Philipp Holl; Vladlen Koltun; Nils Thuerey (NeurIPS, 2022)

Simulating Liquids with Graph Networks. - Jonathan Klimesch; Philipp Holl; Nils Thuerey (CoRR, 2022)

ΦFlow: Differentiable Simulations for PyTorch, TensorFlow and Jax. - Philipp Holl; Nils Thuerey (ICML, 2024)

The Unreasonable Effectiveness of Solving Inverse Problems with Neural Networks. - Philipp Holl; Nils Thuerey (CoRR, 2024)

Φ-ML: Intuitive Scientific Computing with Dimension Types for Jax, PyTorch, TensorFlow & NumPy. - Philipp Holl; Nils Thuerey (J. Open Source Softw., 2024)

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems. - Girnar Goyal; Philipp Holl; Sweta Agrawal; Nils Thuerey (CoRR, 2025)