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DeepMoD: Deep learning for model discovery in noisy data
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
Temporal Normalizing Flows
This paper extends the concept of normalizing flows to so-called temporal Normalizing Flows (tNFs) to estimate time dependent distributions, leveraging the full spatio-temporal information present in the dataset.
Sparsely Constrained Neural Networks for Model Discovery of PDEs
  • G. Both, R. Kusters
  • Computer Science, Physics
    AAAI Spring Symposium: MLPS
  • 9 November 2020
A modular framework that combines deep-learning based approaches with an arbitrary sparse regression technique and demonstrates with several examples that this combination facilitates and enhances model discovery tasks.
Low-loss YIG-based magnonic crystals with large tunable bandgaps
Low-loss spin-wave manipulation in nanometer thick magnonic crystals of discrete YIG stripes separated by air or CoFeB filled grooves exhibiting tunable bandgaps of 50–200 MHz is experimentally demonstrated.
Impact of interaction range and curvature on crystal growth of particles confined to spherical surfaces.
Computer simulations are reported on in which the formation of ribbons at short interaction ranges and packings that incorporate defects if the interaction is longer-ranged are observed, and it is argued that the scaling of the critical crystal size differs slightly from the one proposed in the literature.
Sparsistent Model Discovery
It is shown that the adaptive Lasso will have more chances of verifying the IRC than the Lasso and it is proposed to integrate it within a deep learning model discovery framework with stability selection and error control.
Model discovery in the sparse sampling regime
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
Fully differentiable model discovery
This paper starts by reinterpreting PINNs as multitask models, applying multitask learning using uncertainty, and shows that this leads to a natural framework for including Bayesian regression techniques, and builds a robust model discovery algorithm by using SBL.
Discovering PDEs from Multiple Experiments
A randomised adaptive group Lasso sparsity estimator is introduced to promote grouped sparsity and implement it in a deep learning based PDE discovery framework to create a learning bias that implies the a priori assumption that all experiments can be explained by the same underlying PDE terms with potentially different coefficients.