LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery

• URL: http://arxiv.org/abs/2503.02983v1
• Date: Tue, 04 Mar 2025 20:17:24 GMT
• Title: LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery
• Authors: Cindy Xiangrui Kong, Haoyang Zheng, Guang Lin,
• Abstract summary: Langevin-Assisted Active Physical Discovery (LAPD) is a framework that integrates replica-exchange gradient Langevin Monte Carlo.<n>LAPD achieves reliable, uncertainty-aware identification with noisy data.<n>We evaluate LAPD on diverse nonlinear systems such as the Lotka-Volterra, Lorenz, Burgers, and Convection-Diffusion equations.
• Score: 5.373182035720355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Discovering physical laws from data is a fundamental challenge in scientific research, particularly when high-quality data are scarce or costly to obtain. Traditional methods for identifying dynamical systems often struggle with noise sensitivity, inefficiency in data usage, and the inability to quantify uncertainty effectively. To address these challenges, we propose Langevin-Assisted Active Physical Discovery (LAPD), a Bayesian framework that integrates replica-exchange stochastic gradient Langevin Monte Carlo to simultaneously enable efficient system identification and robust uncertainty quantification (UQ). By balancing gradient-driven exploration in coefficient space and generating an ensemble of candidate models during exploitation, LAPD achieves reliable, uncertainty-aware identification with noisy data. In the face of data scarcity, the probabilistic foundation of LAPD further promotes the integration of active learning (AL) via a hybrid uncertainty-space-filling acquisition function. This strategy sequentially selects informative data to reduce data collection costs while maintaining accuracy. We evaluate LAPD on diverse nonlinear systems such as the Lotka-Volterra, Lorenz, Burgers, and Convection-Diffusion equations, demonstrating its robustness with noisy and limited data as well as superior uncertainty calibration compared to existing methods. The AL extension reduces the required measurements by around 60% for the Lotka-Volterra system and by around 40% for Burgers' equation compared to random data sampling, highlighting its potential for resource-constrained experiments. Our framework establishes a scalable, uncertainty-aware methodology for data-efficient discovery of dynamical systems, with broad applicability to problems where high-fidelity data acquisition is prohibitively expensive.

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