4 citations · 5 across the 8 of their papers we have counts for
8 papers
Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems
Nicolò Botteghi, Silke Glas, Christoph Brune
Constructing reduced-order models (ROMs) capable of efficiently predicting the evolution of parameter-dependent high-dimensional dynamical systems is crucial in many applications i…
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…
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…
Deep Networks are Reproducing Kernel Chains
Tjeerd Jan Heeringa, Len Spek, Christoph Brune
Identifying an appropriate function space for deep neural networks remains a key open question. While shallow neural networks are naturally associated with Reproducing Kernel Banac…
Sparsifying dimensionality reduction of PDE solution data with Bregman learning
Tjeerd Jan Heeringa, Christoph Brune, Mengwu Guo
Classical model reduction techniques project the governing equations onto a linear subspace of the original state space. More recent data-driven techniques use neural networks to e…
Learning a Sparse Representation of Barron Functions with the Inverse Scale Space Flow
Tjeerd Jan Heeringa, Tim Roith, Christoph Brune +1
This paper presents a method for finding a sparse representation of Barron functions. Specifically, given an function , the inverse scale space flow is used to find a spar…
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…
Unsupervised Representation Learning in Deep Reinforcement Learning: A Review
Nicolò Botteghi, Mannes Poel, Christoph Brune
This review addresses the problem of learning abstract representations of the measurement data in the context of Deep Reinforcement Learning (DRL). While the data are often ambiguo…