Related papers: Interpretable Meta-Learning of Physical Systems

Interpretable Meta-Learning of Physical Systems

URL: http://arxiv.org/abs/2312.00477v2
Date: Wed, 20 Mar 2024 15:25:02 GMT
Title: Interpretable Meta-Learning of Physical Systems
Authors: Matthieu Blanke, Marc Lelarge,
Abstract summary: Recent meta-learning methods rely on black-box neural networks, resulting in high computational costs and limited interpretability. We argue that multi-environment generalization can be achieved using a simpler learning model, with an affine structure with respect to the learning task. We demonstrate the competitive generalization performance and the low computational cost of our method by comparing it to state-of-the-art algorithms on physical systems.
Score: 4.343110120255532
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Machine learning methods can be a valuable aid in the scientific process, but they need to face challenging settings where data come from inhomogeneous experimental conditions. Recent meta-learning methods have made significant progress in multi-task learning, but they rely on black-box neural networks, resulting in high computational costs and limited interpretability. Leveraging the structure of the learning problem, we argue that multi-environment generalization can be achieved using a simpler learning model, with an affine structure with respect to the learning task. Crucially, we prove that this architecture can identify the physical parameters of the system, enabling interpreable learning. We demonstrate the competitive generalization performance and the low computational cost of our method by comparing it to state-of-the-art algorithms on physical systems, ranging from toy models to complex, non-analytical systems. The interpretability of our method is illustrated with original applications to physical-parameter-induced adaptation and to adaptive control.

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