no code implementations • 19 Mar 2024 • Janina E. Schütte, Martin Eigel
To improve training efficiency and to enable control of the approximation error, the network mimics an adaptive finite element method (AFEM).
no code implementations • 23 Feb 2024 • David Sommer, Robert Gruhlke, Max Kirstein, Martin Eigel, Claudia Schillings
Sampling from probability densities is a common challenge in fields such as Uncertainty Quantification (UQ) and Generative Modelling (GM).
no code implementations • 5 Feb 2024 • Martin Eigel, Charles Miranda
A novel approach to approximate solutions of Stochastic Differential Equations (SDEs) by Deep Neural Networks is derived and analysed.
no code implementations • 6 Nov 2023 • Charles Miranda, Janina Schütte, David Sommer, Martin Eigel
We sample from a given target distribution by constructing a neural network which maps samples from a simple reference, e. g. the standard normal distribution, to samples from the target.
no code implementations • 1 Apr 2023 • Cosmas Heiß, Ingo Gühring, Martin Eigel
We combine concepts from multilevel solvers for partial differential equations (PDEs) with neural network based deep learning and propose a new methodology for the efficient numerical solution of high-dimensional parametric PDEs.
no code implementations • NeurIPS Workshop DLDE 2021 • Cosmas Heiß, Ingo Gühring, Martin Eigel
In scientific machine learning, neural networks recently have become a popular tool for learning the solutions of differential equations.
Uncertainty Quantification Vocal Bursts Intensity Prediction
1 code implementation • 2 Mar 2021 • Christian Bayer, Martin Eigel, Leon Sallandt, Philipp Trunschke
An efficient compression technique based on hierarchical tensors for popular option pricing methods is presented.