Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.13158 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908567652532224 |
|---|---|
| author | Zimmering, Bernd Coelho, Cecília Gupta, Vaibhav Maleshkova, Maria Niggemann, Oliver |
| author_facet | Zimmering, Bernd Coelho, Cecília Gupta, Vaibhav Maleshkova, Maria Niggemann, Oliver |
| contents | Modelling forced dynamical systems - where an external input drives the system state - is critical across diverse domains such as engineering, finance, and the natural sciences. In this work, we propose Laplace-Net, a decoupled, solver-free neural framework for learning forced and delay-aware systems. It leverages a Laplace transform-based approach to decompose internal dynamics, external inputs, and initial values into established theoretical concepts, enhancing interpretability. Laplace-Net promotes transferability since the system can be rapidly re-trained or fine-tuned for new forcing signals, providing flexibility in applications ranging from controller adaptation to long-horizon forecasting. Experimental results on eight benchmark datasets - including linear, non-linear, and delayed systems - demonstrate the method's improved accuracy and robustness compared to state-of-the-art approaches, particularly in handling complex and previously unseen inputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13158 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Breaking Free: Decoupling Forced Systems with Laplace Neural Networks Zimmering, Bernd Coelho, Cecília Gupta, Vaibhav Maleshkova, Maria Niggemann, Oliver Machine Learning Systems and Control Modelling forced dynamical systems - where an external input drives the system state - is critical across diverse domains such as engineering, finance, and the natural sciences. In this work, we propose Laplace-Net, a decoupled, solver-free neural framework for learning forced and delay-aware systems. It leverages a Laplace transform-based approach to decompose internal dynamics, external inputs, and initial values into established theoretical concepts, enhancing interpretability. Laplace-Net promotes transferability since the system can be rapidly re-trained or fine-tuned for new forcing signals, providing flexibility in applications ranging from controller adaptation to long-horizon forecasting. Experimental results on eight benchmark datasets - including linear, non-linear, and delayed systems - demonstrate the method's improved accuracy and robustness compared to state-of-the-art approaches, particularly in handling complex and previously unseen inputs. |
| title | Breaking Free: Decoupling Forced Systems with Laplace Neural Networks |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2503.13158 |