Latent Field Discovery in Interacting Dynamical Systems with Neural Fields
Authors: Miltiadis (Miltos) Kofinas, Erik Bekkers, Naveen Nagaraja, Efstratios Gavves
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed method, Aether, on settings that include static as well as dynamic fields. First, we explore 2D charged particles that evolve under the effect of a static electrostatic field, as well as 3D particles that evolve under a Lorentz force field [10]. Then, we evaluate our method on a subset of in D [4] that contains a single location, and thus a static field as well. Finally, we explore 3D gravitational n-body problems [5] with dynamic fields. Our code, data, and models will be open-sourced online1. In most experiments, we compare our method against d NRI [14] and Lo CS [24] , two state-of-the-art networks for sequence-to-sequence trajectory forecasting, as well as G-Lo CS. |
| Researcher Affiliation | Collaboration | Miltiadis Kofinas University of Amsterdam m.kofinas@uva.nl Erik J. Bekkers University of Amsterdam e.j.bekkers@uva.nl Naveen Shankar Nagaraja BMW Group Naveen-Shankar.Nagaraja@bmw.de Efstratios Gavves University of Amsterdam egavves@uva.nl |
| Pseudocode | No | The paper provides detailed equations and descriptions of the model architecture and training process in Appendix A, but it does not include explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code, data, and models will be open-sourced online1. 1https://github.com/mkofinas/aether |
| Open Datasets | Yes | We use in D [4], a dataset with real-world traffic scenes that comprises trajectories of pedestrians, vehicles, and cyclists.Du et al. [10] introduced a dataset of 3D charged particles evolving under the influence of a Lorentz force field.We extend the charged particles dataset from Kipf et al. [23] by adding a number of immovable sources. |
| Dataset Splits | Yes | We generate 50,000 simulations for training, 10,000 for validation and 10,000 for testing.The subset corresponds to 12 recordings; we use 8 for training, 2 for validation, and 2 for testing. |
| Hardware Specification | Yes | All experiments were performed on single GPUs. We used 2 different GPU models, namely the Nvidia RTX 2080 Ti, and Nvidia GTX 1080 Ti. |
| Software Dependencies | Yes | Our source code was written in Py Torch [32], version 1.4.0, and CUDA 10.0. |
| Experiment Setup | Yes | Unless specified differently, our neural field has a hidden size of 512. In charged particles and in n-body problems, we only use positions as input to the neural field, while in traffic scenes we also use orientations.Unless stated otherwise, in all experiments, we use a learning rate of 5e 4. |