4 papers
An adjoint method for training data-driven reduced-order models
Donglin Liu, Francisco GarcÃa Atienza, Mengwu Guo
Reduced-order modeling lies at the interface of numerical analysis and data-driven scientific computing, providing principled ways to compress high-fidelity simulations in science…
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…
Towards scientific machine learning for granular material simulations -- challenges and opportunities
Marc Fransen, Andreas Fürst, Deepak Tunuguntla +21
Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights…
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…