3 papers
cs.CE2025
Physics-based deep kernel learning for parameter estimation in high dimensional PDEs
Weihao Yan, Christoph Brune, Mengwu Guo
Inferring parameters of high-dimensional partial differential equations (PDEs) poses significant computational and inferential challenges, primarily due to the curse of dimensional…
cs.LG2025
PDE-DKL: PDE-constrained deep kernel learning in high dimensionality
Weihao Yan, Christoph Brune, Mengwu Guo
Many physics-informed machine learning methods for PDE-based problems rely on Gaussian processes (GPs) or neural networks (NNs). However, both face limitations when data are scarce…
cs.LG2024
PDE-constrained Gaussian process surrogate modeling with uncertain data locations
Dongwei Ye, Weihao Yan, Christoph Brune +1
Gaussian process regression is widely applied in computational science and engineering for surrogate modeling owning to its kernel-based and probabilistic nature. In this work, we…