Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Half-Inverse Gradients for Physical Deep Learning
Authors: Patrick Schnell, Philipp Holl, Nils Thuerey
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on three physical systems: controlling nonlinear oscillators, the Poisson problem, and the quantum dipole problem. |
| Researcher Affiliation | Academia | Patrick Schnell, Philipp Holl and Nils Thuerey Department of Informatics Technical University of Munich Boltzmannstr. 3, 85748 Garching, Germany EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code for the experiments presented in this paper is publicly available at https://github. com/tum-pbs/half-inverse-gradients. |
| Open Datasets | No | The datasets used in the experiments (nonlinear oscillators, Poisson problem, quantum dipole) are custom generated or sampled on-the-fly as described in sections B.2, B.3, and B.4. No public datasets or links to them are provided. |
| Dataset Splits | No | The paper mentions 'training data set' and 'test set' for some experiments (e.g., nonlinear oscillators, quantum dipole) but does not explicitly provide details for a validation split needed for reproduction. |
| Hardware Specification | Yes | Runtimes for the non-linear chain and quantum dipole were measured on a machine with Intel Xeon 6240 CPUs and NVIDIA Ge Force RTX 2080 Ti GPUs. The Poisson experiments used an Intel Xeon W-2235 CPU with NVIDIA Quadro RTX 8000 GPU. |
| Software Dependencies | Yes | As deep learning API we used Tensor Flow version 2.5. |
| Experiment Setup | Yes | Our method depends on several hyperparameters. First, we need a suitable choice of the learning rate. The normalizing effects of HIGs allow for larger learning rates than commonly used gradient descent variants. We are able to use η = 1 for many of our experiments. Second, the batch size b affects the number of data points included in the half-inversion process. |