6 papers
Stochastic Latent Twins: Learning Stochastic Solution Operators
Matthias Chung, Jack Michael Solomon, Deepanshu Verma
Scientific prediction under uncertainty requires more than a single future state: it requires the distribution of possible outcomes. We show that this distribution-valued evolution…
Latent Twin Operator
Deepanshu Verma, Riley Chen, Matthias Chung
Surrogate models deployed on real physical systems rarely see data at fixed resolutions: sensor configurations vary across deployments and may evolve over time as sensing infrastru…
Physics-conforming Latent Twins
Matthias Chung, Yutong Bu, Deepanshu Verma
Surrogate models are central to scientific machine learning, where they enable fast prediction, simulation, inference, and control for complex physical systems. For time-dependent…
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…
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…
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)…