170 citations · 324 across the 15 of their papers we have counts for
17 papers
Data-driven cold starting of good reservoirs
Lyudmila Grigoryeva, Boumediene Hamzi, Felix P. Kemeth +4
Using short histories of observations from a dynamical system, a workflow for the post-training initialization of reservoir computing systems is described. This strategy is called…
Black and Gray Box Learning of Amplitude Equations: Application to Phase Field Systems
Felix P. Kemeth, Sergio Alonso, Blas Echebarria +3
We present a data-driven approach to learning surrogate models for amplitude equations, and illustrate its application to interfacial dynamics of phase field systems. In particular…
Questionnaires to PDEs: From Disorganized Data to Emergent Generative Dynamic Models
David W. Sroczynski, Felix P. Kemeth, Ronald R. Coifman +1
Starting with sets of disorganized observations of spatially varying and temporally evolving systems, obtained at different (also disorganized) sets of parameters, we demonstrate t…
Global and Local Reduced Models for Interacting, Heterogeneous Agents
Thomas N. Thiem, Felix P. Kemeth, Tom Bertalan +2
Large collections of coupled, heterogeneous agents can manifest complex dynamical behavior presenting difficulties for simulation and analysis. However, if the collective dynamics…
Initializing LSTM internal states via manifold learning
Felix P. Kemeth, Tom Bertalan, Nikolaos Evangelou +3
We present an approach, based on learning an intrinsic data manifold, for the initialization of the internal state values of LSTM recurrent neural networks, ensuring consistency wi…
Learning emergent PDEs in a learned emergent space
Felix P. Kemeth, Tom Bertalan, Thomas Thiem +4
We extract data-driven, intrinsic spatial coordinates from observations of the dynamics of large systems of coupled heterogeneous agents. These coordinates then serve as an emergen…
Coarse-grained and emergent distributed parameter systems from data
Hassan Arbabi, Felix P. Kemeth, Tom Bertalan +1
We explore the derivation of distributed parameter system evolution laws (and in particular, partial differential operators and associated partial differential equations, PDEs) fro…
Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram Signal Classification
Maximilian P Oppelt, Maximilian Riehl, Felix P Kemeth +1
An essential part for the accurate classification of electrocardiogram (ECG) signals is the extraction of informative yet general features, which are able to discriminate diseases.…
2-Cluster Fixed-Point Analysis of Mean-Coupled Stuart-Landau Oscillators in the Center Manifold
Felix P. Kemeth, Bernold Fiedler, Sindre W. Haugland +1
We reduce the dynamics of an ensemble of mean-coupled Stuart-Landau oscillators close to the synchronized solution. In particular, we map the system onto the center manifold of the…
Global heteroclinic rebel dynamics among large 2-clusters in permutation equivariant systems
Bernold Fiedler, Sindre W. Haugland, Felix P. Kemeth +1
We explore equivariant dynamics under the symmetric group of all permutations of elements. Specifically we study one-parameter vector fields, up to cubic order, which com…
The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks
Faezeh Nejati Hatamian, Nishant Ravikumar, Sulaiman Vesal +3
Cardiovascular diseases are the most common cause of mortality worldwide. Detection of atrial fibrillation (AF) in the asymptomatic stage can help prevent strokes. It also improves…
NeurIPS 2019 Disentanglement Challenge: Improved Disentanglement through Learned Aggregation of Convolutional Feature Maps
Maximilian Seitzer, Andreas Foltyn, Felix P. Kemeth
This report to our stage 2 submission to the NeurIPS 2019 disentanglement challenge presents a simple image preprocessing method for learning disentangled latent factors. We propos…
Lyapunov Analysis of Chimera States in Globally Coupled Stuart-Landau oscillators
Kevin Höhlein, Felix P. Kemeth, Katharina Krischer
Oscillatory systems with long-range or global coupling offer promising insight into the interplay between high-dimensional (or microscopic) chaotic motion and collective interactio…
Cluster singularity: the unfolding of clustering behavior in globally coupled Stuart-Landau oscillators
Felix P. Kemeth, Sindre W. Haugland, Katharina Krischer
The ubiquitous occurrence of cluster patterns in nature still lacks a comprehensive understanding. It is known that the dynamics of many such natural systems is captured by ensembl…
Symmetries of Chimera States
Felix P. Kemeth, Sindre W. Haugland, Katharina Krischer
Symmetry broken states arise naturally in oscillatory networks. In this Letter, we investigate chaotic attractors in an ensemble of four mean-coupled Stuart-Landau oscillators with…
An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning
Felix P. Kemeth, Sindre W. Haugland, Felix Dietrich +7
Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations; these are, in turn, use…
A classification scheme for chimera states
Felix P. Kemeth, Sindre W. Haugland, Lennart Schmidt +2
We present a universal characterization scheme for chimera states applicable to both numerical and experimental data sets. The scheme is based on two correlation measures that enab…