Fetching the paper…
Reading the bibliography…
The discovery of governing differential equations from data is an open frontier in machine learning.
On the Convergence of Adam and Beyond, Apr. 2019
S. J. Reddi, S. Kale, and S. Kumar · 1904
Earlier work this paper cites.
Dynamical Variational Autoencoders: A Comprehensive Review
L. Girin, S. Leglaive, X. Bie, J. Diard, T. Hueber, and X. Alameda-Pineda · 1935
Earlier work this paper cites.
An Introduction to Variational Autoencoders
D. P. Kingma and M. Welling · 1935
Earlier work this paper cites.
Variational Inference with Normalizing Flows
D. Rezende and S. Mohamed · 1938
Earlier work this paper cites.
Random Dynamical Systems
L. Arnold · 1998
Earlier work this paper cites.
Weak SINDy: Galerkin-Based Data-Driven Model Selection
D. A. Messenger and D. M. Bortz · 2005
Earlier work this paper cites.
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks, Mar. 2022
Y. Yacoby, W. Pan, and F. Doshi-Velez · 2007
Earlier work this paper cites.
D. Nguyen, S. Ouala, L. Drumetz, and R. Fablet · 2009
Earlier work this paper cites.
Distilling Free-Form Natural Laws from Experimental Data
M. Schmidt and H. Lipson · 2009
Earlier work this paper cites.
Approaching complexity by stochastic methods: From biological systems to turbulence
R. Friedrich, J. Peinke, M. Sahimi, and M. Reza Rahimi Tabar · 2011
Earlier work this paper cites.
Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems, Mar. 2021
R. Lopez and P. J. Atzberger · 2012
Earlier work this paper cites.
Nonautonomous Dynamical Systems in the Life Sciences , volume 2102 of Lecture Notes in Mathematics
P. E. Kloeden and C. Pötzsche, editors · 2013
Earlier work this paper cites.
Auto-Encoding Variational Bayes, May 2014
D. P. Kingma and M. Welling · 2014
Earlier work this paper cites.
An Introduction to Stochastic Dynamics
J. Duan · 2015
Earlier work this paper cites.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
S. L. Brunton, J. L. Proctor, and J. N. Kutz · 2016
Earlier work this paper cites.
Applied Nonautonomous and Random Dynamical Systems
T. Caraballo and X. Han · 2016
Earlier work this paper cites.
D. Ha, A. Dai, and Q. V. Le · 2016
Earlier work this paper cites.
Random Ordinary Differential Equations and Their Numerical Solution , volume 85 of Probability Theory and Stochastic Modelling
X. Han and P. E. Kloeden · 2017
Cited alongside, same era.
Categorical Reparameterization with Gumbel-Softmax, Aug. 2017
E. Jang, S. Gu, and B. Poole · 2017
Cited alongside, same era.
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, Mar. 2017
C. J. Maddison, A. Mnih, and Y. W. Teh · 2017
Cited alongside, same era.
Sparse learning of stochastic dynamical equations
L. Boninsegna, F. Nüske, and C. Clementi · 2018
Cited alongside, same era.
Learning Sparse Neural Networks through $L_0$ Regularization, June 2018
C. Louizos, M. Welling, and D. P. Kingma · 2018
Nonlinear stochastic modelling with Langevin regression
J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton · 2021
Later among the works it cites.
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
U. Fasel, J. N. Kutz, B. W. Brunton, and S. L. Brunton · 2021
Later among the works it cites.
Identifying Latent Stochastic Differential Equations
A. Hasan, J. M. Pereira, S. Farsiu, and V. Tarokh · 2021
Later among the works it cites.
Sparsifying Priors for Bayesian Uncertainty Quantification in Model Discovery, July 2021
S. M. Hirsh, D. A. Barajas-Solano, and J. N. Kutz · 2021
Later among the works it cites.
Physics-informed machine learning
G. E. Karniadakis, I. G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, and L. Yang · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Implicit Weight Uncertainty in Neural Networks, May 2018
N. Pawlowski, A. Brock, M. C. H. Lee, M. Rajchl, and B. Glocker · 2018
Cited alongside, same era.
A Unified Framework for Sparse Relaxed Regularized Regression: SR3
P. Zheng, T. Askham, S. L. Brunton, J. N. Kutz, and A. Y. Aravkin · 2018
Cited alongside, same era.
Improved Conditional VRNNs for Video Prediction
L. Castrejon, N. Ballas, and A. Courville · 2019
Cited alongside, same era.
Decoupled Weight Decay Regularization, Jan. 2019
I. Loshchilov and F. Hutter · 2019
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
Cited alongside, same era.
Applied Stochastic Differential Equations
S. Särkkä and A. Solin · 2019
Cited alongside, same era.
Time-series Generative Adversarial Networks
J. Yoon, D. Jarrett, and M. van der Schaar · 2019
Cited alongside, same era.
On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression
R. Nayek, R. Fuentes, K. Worden, and E. J. Cross · 2021
Later among the works it cites.
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling
N. Takeishi and A. Kalousis · 2021
Later among the works it cites.
Amortized Variational Inference: Towards the Mathematical Foundation and Review, Sept. 2022
A. Ganguly, S. Jain, and U. Watchareeruetai · 2022
Later among the works it cites.
Stochastic embeddings of dynamical phenomena through variational autoencoders
C. A. García, P. Félix, J. M. Presedo, and A. Otero · 2022
Later among the works it cites.
PySINDy: A comprehensive Python package for robust sparse system identification
A. A. Kaptanoglu, B. M. d. Silva, U. Fasel, K. Kaheman, A. J. Goldschmidt, J. Callaham, C. B. Delahunt, Z. G. Nicolaou, K. Champion, J.-C. Loiseau, J. N. Kutz, and S. L. Brunton · 2022
Later among the works it cites.
A. Nabeel, A. Karichannavar, S. Palathingal, J. Jhawar, D. R. M, and V. Guttal · 2022
Later among the works it cites.
A sparse Bayesian framework for discovering interpretable nonlinear stochastic dynamical systems with Gaussian white noise
T. Tripura and S. Chakraborty · 2022
Later among the works it cites.
Data-Driven Discovery of Stochastic Differential Equations
Y. Wang, H. Fang, J. Jin, G. Ma, X. He, X. Dai, Z. Yue, C. Cheng, H.-T. Zhang, D. Pu, D. Wu, Y. Yuan, J. Gonçalves, J. Kurths, and H. Ding · 2022
Later among the works it cites.
PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations
W. Zhong and H. Meidani · 2022
Later among the works it cites.
Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery, Jan. 2023
L. M. Gao, U. Fasel, S. L. Brunton, and J. N. Kutz · 2023
Closest in time.
Sparse inference and active learning of stochastic differential equations from data
Y. Huang, Y. Mabrouk, G. Gompper, and B. Sabass · 2045
Closest in time.