12 citations · 13 across the 13 of their papers we have counts for
12 papers · 1 filter
Fast Reconstruction of Exact Maxwell Dynamics from Sparse Data
Dan DeGenaro, Xin Li, Obed Amo +4
We introduce FLASH-MAX, a shallow, exact-by-construction neural network architecture for predicting homogeneous electromagnetic fields from sparse pointwise observations. Each hidd…
Comparing EPGP Surrogates and Finite Elements Under Degree-of-Freedom Parity
Obed Amo, Samit Ghosh, Markus Lange-Hegermann +2
We present a new benchmarking study comparing a boundary-constrained Ehrenpreis--Palamodov Gaussian Process (B-EPGP) surrogate with a classical finite element method combined with…
Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series
Juhi Soni, Markus Lange-Hegermann, Stefan Windmann
We propose an unsupervised anomaly detection approach based on a physics-informed diffusion model for multivariate time series data. Over the past years, diffusion model has demons…
Approximately Equivariant Recurrent Generative Models for Quasi-Periodic Time Series with a Progressive Training Scheme
Ruwen Fulek, Markus Lange-Hegermann
We present a simple yet effective generative model for time series, based on a Recurrent Variational Autoencoder that we refer to as AEQ-RVAE-ST. Recurrent layers often struggle wi…
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes
Markus Lange-Hegermann, Christoph Zimmer
Experimental exploration of high-cost systems with safety constraints, common in engineering applications, is a challenging endeavor. Data-driven models offer a promising solution,…
PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning
Henrik Folz, Joshua Henjes, Annika Heuer +7
In this paper, we explore the optimization of metal recycling with a focus on real-time differentiation between alloys of copper and aluminium. Spectral data, obtained through Prom…