arXiv:2606.11251v1 Announce Type: new Abstract: Many multivariate dynamical systems are observed only through trajectories, leaving the mechanisms governing their joint dynamics hidden. Existing approaches can impose interpretable dynamics or learn flexible state transitions, yet the resulting interaction structure is typically either specified in advance or left implicit within the learned dynamics. We introduce MF-Net, a recurrent dynamical model that represents all variables in a shared field state and updates this state through a learned relation law. Each variable carries a field componen
The paper leverages recent advancements in neural network architectures and dynamical systems theory to address the challenge of hidden mechanisms in multivariate systems.
This development proposes a novel recurrent neural network architecture that could significantly improve the understanding and prediction of complex dynamical systems across scientific and engineering domains.
Traditional approaches often either pre-specify interaction structures or leave them implicit, whereas MF-Net aims to learn these structures and relationships within a shared field state.
- · AI researchers
- · Robotics
- · Complex systems modeling
- · Scientific simulation
- · Traditional mechanistic modeling
- · Statistical pattern recognition without structural insight
Improved predictive models for various multivariate systems by explicitly learning interaction structures.
Accelerated discovery and design in fields like materials science, drug discovery, and climate modeling due to better system understanding.
Enhanced autonomous agents capable of navigating and manipulating complex environments with more nuanced understanding of underlying dynamics.
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Read at arXiv cs.LG