3 papers
cs.LG2026
Neural Optimal Design of Experiment for Inverse Problems
John E. Darges, Babak Maboudi Afkham, Matthias Chung
We introduce Neural Optimal Design of Experiments, a learning-based framework for optimal experimental design in inverse problems that avoids classical bilevel optimization and ind…
cs.LG2025
Latent Twins
Matthias Chung, Deepanshu Verma, Max Collins +3
Over the past decade, scientific machine learning has transformed the development of mathematical and computational frameworks for analyzing, modeling, and predicting complex syste…
cs.LG2024
Paired Wasserstein Autoencoders for Conditional Sampling
Moritz Piening, Matthias Chung
Generative autoencoders learn compact latent representations of data distributions through jointly optimized encoder--decoder pairs. In particular, Wasserstein autoencoders (WAEs)…