Department Seminars & Colloquia

Category 학과 Seminar/ Colloquium
Event ACM Seminars
Title Efficient Bayesian physics informed neural networks for inverse problems via ensemble Kalman inversion
Abstract Bayesian Physics Informed Neural Networks (B-PINNs) have gained significant attention for inferring physical parameters and learning the forward solutions for problems based on partial differential equations. However, the overparameterized nature of neural networks poses a computational challenge for high-dimensional posterior inference. Existing inference approaches, such as particle-based or variance inference methods, are either computationally expensive for highdimensional posterior inference or provide unsatisfactory uncertainty estimates. In this paper, we present a new efficient inference algorithm for B-PINNs that uses Ensemble Kalman Inversion (EKI) for high-dimensional inference tasks. By reframing the setup of B-PINNs as a traditional Bayesian inverse problem, we can take advantage of EKI’s key features: (1) gradient-free, (2) computational complexity scales linearly with the dimension of the parameter spaces, and (3) rapid convergence with typically O(100) iterations. We demonstrate the applicability and performance of the proposed method through various types of numerical examples. We find that our proposed method can achieve inference results with informative uncertainty estimates comparable to Hamiltonian Monte Carlo (HMC)-based B-PINNs with a much reduced computational cost. These findings suggest that our proposed approach has great potential for uncertainty quantification in physics-informed machine learning for practical applications.
Daytime 2023-03-31 (Fri) / 12:00 ~ 13:00 ** 날짜에 유의하세요. **
Place Zoom: https://kaist.zoom.us/j/87516570701
Language English
Speaker`s name Xueyu Zhu
Speakers`s Affiliation University of Iowa
Speaker`s homepage
Other information
Hosts 신연종
URL https://sites.google.com/view/kaist-acm
담당자 박준서
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