SNODE: Spectral Discretization of Neural ODEs for System Identification
Authors: Alessio Quaglino, Marco Gallieri, Jonathan Masci, Jan Koutník
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental comparison to standard methods, such as backpropagation through explicit solvers and the adjoint technique (Chen et al., 2018), on training surrogate models of small and medium-scale dynamical systems shows that it is at least one order of magnitude faster at reaching a comparable value of the loss function. |
| Researcher Affiliation | Industry | NNAISENSE, Lugano, Switzerland {alessio, marco, jonathan, jan}@nnaisense.com |
| Pseudocode | Yes | Algorithm 1 δ-SNODE training... Algorithm 2 α-SNODE training |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic data for its experiments (e.g., vehicle dynamics, multi-agent simulation) but does not provide access information or refer to a publicly available dataset for training. |
| Dataset Splits | No | The paper refers to 'cross-validation' conceptually but does not provide specific train/validation/test dataset splits (e.g., percentages, sample counts, or citations to predefined splits) for the experiments conducted. |
| Hardware Specification | Yes | Experiments were performed on a i9 Apple laptop with 32GB of RAM. |
| Software Dependencies | No | The paper mentions software like ADAM, SGD, and PyTorch for automatic differentiation, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Time horizon T = 10s and batch size of 100 were used. Learning rates were set to 10 2 for ADAM (for all methods) and 10 3 for SGD (for α-SNODE). For the α-SNODE method, γ = 3 and 10 iterations were used for the SGD and ADAM algorithms at each epoch, as outlined in Algorithm 2. |