Learning Hybrid Dynamics Models with Simulator-Informed Latent States
Authors: Katharina Ensinger, Sebastian Ziesche, Sebastian Trimpe
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments In this section, we show that: (i) Our KKL-RNN achieves equal or higher accuracy than baselines, especially in the partially OVS case; (ii) We learn a plausible split in OVS and non-OVS components with our method; (iii) Our method can buffer missing simulator inputs; (iv) We can easily incorporate properties as a decaying non-OVS part; (v) The concept can also be leveraged in the pure learning-based scenario. Table 1: RMSEs for Systems i)-iii) (mean (std)) over 5 independent runs. |
| Researcher Affiliation | Collaboration | 1 Bosch Center for Artificial Intelligence, Renningen, Germany 2 Institute for Data Science in Mechanical Engineering, RWTH Aachen Univeristy |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | ii) Double-torsion pendulum: We consider the measurements and the corresponding numerical simulation from Lisowski et al. (2020) and use the first 150 steps for training. ... iii) Drill-string system: We train on measurements provided in Aarsnes and Shor (2017) Fig. 14 and the corresponding simulator. |
| Dataset Splits | No | The paper mentions training data and performing predictions on the full trajectory, but it does not specify explicit training/validation/test splits, percentages, or absolute sample counts for each split. It uses a 'warmup phase' but this is not a dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a gated recurrent unit (GRU) but does not provide specific version numbers for software, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | The first R steps ˆy0:R are used as a warmup phase for the non-OVS residuum to obtain appropriate latent states. We train our model by computing an N-step rollout z0:N, yv 0:N and y0:N via Eq. (9) and minimizing the loss ˆθ = arg min θ y0:N ˆy0:N 2 + s0:N ˆs0:N 2 +λ yv 0:N 2, (10) where 2 denotes the MSE. We introduce a regularization factor λ R that allows to balance the influence of the learning-based component as it is typical for hybrid models (Takeishi and Kalousis 2021; Yin et al. 2021). Here, we learn T θ with an MLP... The trainable observation models gθ, hθ and rθ are modeled as linear layers. Here, we model the non OVS part f v θ in Eq. (8) with a gated recurrent unit (GRU). In the experiments, we apply exponential damping by bounding the RNN observation model rθ with an appropriate activation function and multiplying it with exp( at). |