Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Kusters, Remy"'
Discovering interpretable patterns for classification of sequential data is of key importance for a variety of fields, ranging from genomics to fraud detection or more generally interpretable decision-making. In this paper, we propose a novel differe
Externí odkaz:
http://arxiv.org/abs/2302.11286
Rule-based decision models are attractive due to their interpretability. However, existing rule induction methods often result in long and consequently less interpretable rule models. This problem can often be attributed to the lack of appropriately
Externí odkaz:
http://arxiv.org/abs/2201.06515
Automated model discovery of partial differential equations (PDEs) usually considers a single experiment or dataset to infer the underlying governing equations. In practice, experiments have inherent natural variability in parameters, initial and bou
Externí odkaz:
http://arxiv.org/abs/2109.11939
Discovering the partial differential equations underlying spatio-temporal datasets from very limited and highly noisy observations is of paramount interest in many scientific fields. However, it remains an open question to know when model discovery a
Externí odkaz:
http://arxiv.org/abs/2106.11936
Autor:
Both, Gert-Jan, Kusters, Remy
Model discovery aims at autonomously discovering differential equations underlying a dataset. Approaches based on Physics Informed Neural Networks (PINNs) have shown great promise, but a fully-differentiable model which explicitly learns the equation
Externí odkaz:
http://arxiv.org/abs/2106.04886
To improve the physical understanding and the predictions of complex dynamic systems, such as ocean dynamics and weather predictions, it is of paramount interest to identify interpretable models from coarsely and off-grid sampled observations. In thi
Externí odkaz:
http://arxiv.org/abs/2105.00400
Sparse regression on a library of candidate features has developed as the prime method to discover the partial differential equation underlying a spatio-temporal data-set. These features consist of higher order derivatives, limiting model discovery t
Externí odkaz:
http://arxiv.org/abs/2011.04336
Autor:
Both, Gert-Jan, Kusters, Remy
Analyzing and interpreting time-dependent stochastic data requires accurate and robust density estimation. In this paper we extend the concept of normalizing flows to so-called temporal Normalizing Flows (tNFs) to estimate time dependent distribution
Externí odkaz:
http://arxiv.org/abs/1912.09092
Autor:
Michaels, Thomas C. T., Kusters, Remy, Dear, Alexander J., Storm, Cornelis, Weaver, James C., Mahadevan, L.
Localized deformation patterns are a common motif in morphogenesis and are increasingly finding widespread applications in materials science, for instance as memory devices. Here we describe the emergence of spatially localized deformations in a mini
Externí odkaz:
http://arxiv.org/abs/1906.04638
We introduce DeepMoD, a Deep learning based Model Discovery algorithm. DeepMoD discovers the partial differential equation underlying a spatio-temporal data set using sparse regression on a library of possible functions and their derivatives. A neura
Externí odkaz:
http://arxiv.org/abs/1904.09406