3 papers
cs.DC2026
Multi-Dataset Inverse Problem Solving with Distributed Generative AI
Daniel Lersch, Steven Goldenberg, Johann Rudi +5
Extracting a shared set of unknown, not directly measurable quantities from multiple, heterogeneous datasets is a common challenge across scientific domains. A prominent example is…
math.NA2025
Neural Networks for Bayesian Inverse Problems Governed by a Nonlinear ODE
German Villalobos, Johann Rudi, Andreas Mang
We investigate the use of neural networks (NNs) for the estimation of hidden model parameters and uncertainty quantification from noisy observational data for inverse parameter est…
cs.LG2025
Multidimensional Distributional Neural Network Output Demonstrated in Super-Resolution of Surface Wind Speed
Harrison J. Goldwyn, Mitchell Krock, Johann Rudi +2
Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While exis…