DeepMoD: deep learning for model discovery in noisy data
From MaRDI portal
Publication:2128343
DOI10.1016/j.jcp.2020.109985OpenAlexW2937653888MaRDI QIDQ2128343
Pierre Sens, Remy Kusters, Subham Choudhury, Gert-Jan Both
Publication date: 21 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1904.09406
Related Items
Revealing hidden dynamics from time-series data by ODENet, Data-driven sparse identification of nonlinear dynamical systems using linear multistep methods, A method for computing inverse parametric PDE problems with random-weight neural networks, Discovering stochastic partial differential equations from limited data using variational Bayes inference, A Review of Data‐Driven Discovery for Dynamic Systems, Bayesian deep learning for partial differential equation parameter discovery with sparse and noisy data, On mathematical modeling in image reconstruction and beyond, Forecasting of nonlinear dynamics based on symbolic invariance, Bayesian learning of stochastic dynamical models, A Bayesian approach for data-driven dynamic equation discovery, Verification and accuracy check of simulations with pope and ipope
Uses Software
Cites Work
- A stochastic Keller-Segel model of chemotaxis
- Initiation of slime mold aggregation viewed as an instability
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
- A neural network approach for the blind deconvolution of turbulent flows
- Image Super-Resolution Via Sparse Representation
