We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification. When applicable, we also incorporate domain-specific physical constraints to improve the accuracy of the NSSM. The major benefit of our approach is that instead of relying solely on data from a single target system, our framework utilizes data from a diverse set of source systems, enabling learning from limited target data, as well as with few online training iterations. Through benchmark examples, we demonstrate the potential of our approach, study the effect of fine-tuning subnetworks rather than full fine-tuning, and report real-world case studies to illustrate the practical application and generalizability of the approach to practical problems with physical-constraints. Specifically, we show that the meta-learned models result in improved downstream performance in model-based state estimation in indoor localization and energy systems.
Manifold meta-learning for reduced-complexity neural system identification
SUPSI（瑞士应用科学大学）；IDSIA - Dalle Molle Institute for Artificial Intelligence（达勒·莫勒人工智能研究所）；Mitsubishi Electric Research Laboratories (MERL)（三菱电机美国研究实验室）；IMT School for Advanced Studies Lucca（卢卡高等研究院）
Identification of state functions by physically-guided neural networks with physically-meaningful internal layers
Aragón Institute of Engineering Research (I3A)（阿拉贡工程研究所（I3A））；University of Zaragoza（萨拉戈萨大学）；Escuela de Ingeniería y Arquitectura (EINA)（工程与建筑学院（EINA））
Learning Physically Consistent Lagrangian Control Models Without Acceleration Measurements
Sorbonne University（索邦大学）；Institut des Systèmes Intelligents et de Robotique（智能系统与机器人研究所）；Criteo AI Lab Paris（Criteo巴黎AI实验室）；Centre of Robotics（机器人中心）；MINES Paris-PSL PSL University（巴黎矿学院PSL大学）
Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification
University Hospital Erlangen（埃尔朗根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN（埃尔朗根-EMN综合癌症中心）
