2 citations · 3 across the 5 of their papers we have counts for
5 papers
Accelerating Full Waveform Inversion By Transfer Learning
Divya Shyam Singh, Leon Herrmann, Qing Sun +3
Full waveform inversion (FWI) is a powerful tool for reconstructing material fields based on sparsely measured data obtained by wave propagation. For specific problems, discretizin…
On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among them
David W. Sroczynski, Felix Dietrich, Eleni D. Koronaki +4
Before we attempt to learn a function between two (sets of) observables of a physical process, we must first decide what the inputs and what the outputs of the desired function are…
Systematic construction of continuous-time neural networks for linear dynamical systems
Chinmay Datar, Adwait Datar, Felix Dietrich +1
Discovering a suitable neural network architecture for modeling complex dynamical systems poses a formidable challenge, often involving extensive trial and error and navigation thr…
Transporting Densities Across Dimensions
Michael Plainer, Felix Dietrich, Ioannis G. Kevrekidis
Even the best scientific equipment can only partially observe reality. Recorded data is often lower-dimensional, e.g., two-dimensional pictures of the three-dimensional world. Comb…
Safe Policy Improvement Approaches and their Limitations
Philipp Scholl, Felix Dietrich, Clemens Otte +1
Safe Policy Improvement (SPI) is an important technique for offline reinforcement learning in safety critical applications as it improves the behavior policy with a high probabilit…