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DeepMoD: Deep learning for Model Discovery in noisy data

20 April 2019

Papers citing "DeepMoD: Deep learning for Model Discovery in noisy data"

50 / 52 papers shown

Title
Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models Congcong Zhu Xiaoyan Xu Jiayue Han Jingrun Chen OOD AI4CE 163 0 0 16 May 2025
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis Yasamin Jalalian Juan Felipe Osorio Ramirez Alexander W. Hsu Bamdad Hosseini H. Owhadi 182 0 0 02 Mar 2025
DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Yaohua Zang P. Koutsourelakis AI4CE 134 1 0 10 Feb 2025
ADAM-SINDy: An Efficient Optimization Framework for Parameterized Nonlinear Dynamical System Identification Siva Viknesh Younes Tatari Amirhossein Arzani 113 3 0 21 Oct 2024
On the estimation rate of Bayesian PINN for inverse problems Yi Sun Debarghya Mukherjee Yves Atchadé PINN 112 1 0 21 Jun 2024
GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of Nonlinear Partial Differential Equations Ali Forootani Peter Benner 57 1 0 14 May 2024
An invariance constrained deep learning network for PDE discovery Chao Chen Hui Li Xiaowei Jin PINN 45 1 0 06 Feb 2024
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels Da Long Wei W. Xing Aditi S. Krishnapriyan R. Kirby Shandian Zhe Michael W. Mahoney 57 0 0 09 Oct 2023
Physics-constrained robust learning of open-form partial differential equations from limited and noisy data Mengge Du Yuntian Chen Longfeng Nie Siyu Lou Dong-juan Zhang AI4CE 85 8 0 14 Sep 2023
A Robust SINDy Approach by Combining Neural Networks and an Integral Form Ali Forootani P. Goyal P. Benner 103 4 0 13 Sep 2023
Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data R. Stephany Christopher Earls 61 4 0 09 Sep 2023
Learning minimal representations of stochastic processes with variational autoencoders Gabriel Fernández-Fernández Carlo Manzo M. Lewenstein A. Dauphin Gorka Muñoz-Gil DiffM 62 6 0 21 Jul 2023
Discovering stochastic partial differential equations from limited data using variational Bayes inference Yogesh Chandrakant Mathpati Tapas Tripura R. Nayek S. Chakraborty DiffM 58 6 0 28 Jun 2023
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl M. Cranmer 122 42 0 02 May 2023
Information Theory Inspired Pattern Analysis for Time-series Data Yushan Huang Yuchen Zhao Alexander Capstick Francesca Palermo Hamed Haddadi Payam Barnaghi AI4TS 56 1 0 22 Feb 2023
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery T. Tang Simon Mak David B. Dunson 62 4 0 01 Feb 2023
Generalized Neural Closure Models with Interpretability Abhinava Gupta Pierre FJ Lermusiaux AI4CE 78 9 0 15 Jan 2023
Harmonic (Quantum) Neural Networks Atiyo Ghosh Antonio A. Gentile M. Dagrada Chul Lee S. Kim Hyukgeun Cha Yunjun Choi Brad Kim J. Kye V. Elfving AI4CE 76 1 0 14 Dec 2022
PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data R. Stephany Christopher Earls 46 18 0 09 Dec 2022
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty Luning Sun Daniel Zhengyu Huang Hao Sun Jian-Xun Wang 75 10 0 14 Oct 2022
A Method for Computing Inverse Parametric PDE Problems with Random-Weight Neural Networks S. Dong Yiran Wang 66 22 0 09 Oct 2022
Weak Collocation Regression method: fast reveal hidden stochastic dynamics from high-dimensional aggregate data Liwei Lu Zhijun Zeng Yan Jiang Yi Zhu Pipi Hu 92 4 0 06 Sep 2022
Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion Hao Xu Junsheng Zeng Dongxiao Zhang DiffM 71 20 0 05 Aug 2022
Noise-aware Physics-informed Machine Learning for Robust PDE Discovery Pongpisit Thanasutives Takeshi Morita M. Numao Ken-ichi Fukui PINN AI4CE 125 19 0 26 Jun 2022
Rediscovering orbital mechanics with machine learning Pablo Lemos N. Jeffrey M. Cranmer S. Ho Peter W. Battaglia PINN AI4CE 84 92 0 04 Feb 2022
Learning deterministic hydrodynamic equations from stochastic active particle dynamics Suryanarayana Maddu Q. Vagne I. Sbalzarini AI4CE 23 4 0 21 Jan 2022
Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter D. Wadekar L. Thiele F. Villaescusa-Navarro J. Hill M. Cranmer D. Spergel N. Battaglia D. Anglés-Alcázar L. Hernquist S. Ho 114 12 0 04 Jan 2022
Quantum Model-Discovery Niklas Heim Atiyo Ghosh Oleksandr Kyriienko V. Elfving 70 11 0 11 Nov 2021
PDE-READ: Human-readable Partial Differential Equation Discovery using Deep Learning R. Stephany Christopher Earls DiffM AIMat 87 29 0 01 Nov 2021
Discovering PDEs from Multiple Experiments Georges Tod G. Both R. Kusters 138 1 0 24 Sep 2021
Learning Dynamics from Noisy Measurements using Deep Learning with a Runge-Kutta Constraint P. Goyal P. Benner 81 9 0 23 Sep 2021
Data-Driven Theory-guided Learning of Partial Differential Equations using SimultaNeous Basis Function Approximation and Parameter Estimation (SNAPE) Sutanu Bhowmick Satish Nagarajaiah 51 7 0 14 Sep 2021
Bayesian Deep Learning for Partial Differential Equation Parameter Discovery with Sparse and Noisy Data Christophe Bonneville Christopher Earls 96 14 0 05 Aug 2021
Physics-constrained Deep Learning for Robust Inverse ECG Modeling Jianxin Xie B. Yao 82 22 0 26 Jul 2021
Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential Equations Zhiming Zhang Yongming Liu 54 11 0 08 Jul 2021
Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks Suryanarayana Maddu D. Sturm Christian L. Müller I. Sbalzarini AI4CE 94 84 0 02 Jul 2021
Sparsistent Model Discovery Georges Tod G. Both R. Kusters 105 1 0 22 Jun 2021
Fully differentiable model discovery G. Both R. Kusters PINN 77 2 0 09 Jun 2021
Robust discovery of partial differential equations in complex situations Hao Xu Dongxiao Zhang AI4CE 87 29 0 31 May 2021
Physics-informed Spline Learning for Nonlinear Dynamics Discovery Fangzheng Sun Yang Liu Hao Sun AI4CE 69 28 0 05 May 2021
Model discovery in the sparse sampling regime G. Both Georges Tod R. Kusters 59 3 0 02 May 2021
Identifying Physical Law of Hamiltonian Systems via Meta-Learning Seungjun Lee Haesang Yang W. Seong 61 13 0 23 Feb 2021
Learning physically consistent mathematical models from data using group sparsity Suryanarayana Maddu B. Cheeseman Christian L. Müller I. Sbalzarini 54 5 0 11 Dec 2020
Sparsely constrained neural networks for model discovery of PDEs G. Both Gijs Vermarien R. Kusters 20 5 0 09 Nov 2020
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data Kadierdan Kaheman Steven L. Brunton J. Nathan Kutz 85 84 0 12 Sep 2020
Hypersolvers: Toward Fast Continuous-Depth Models Michael Poli Stefano Massaroli Atsushi Yamashita Hajime Asama Jinkyoo Park BDL AI4CE 74 47 0 19 Jul 2020
Discovering Symbolic Models from Deep Learning with Inductive Biases M. Cranmer Alvaro Sanchez-Gonzalez Peter W. Battaglia Rui Xu Kyle Cranmer D. Spergel S. Ho AI4CE 96 483 0 19 Jun 2020
Physics informed deep learning for computational elastodynamics without labeled data Chengping Rao Hao Sun Yang Liu PINN AI4CE 94 227 0 10 Jun 2020
Revealing hidden dynamics from time-series data by ODENet Pipi Hu Wuyue Yang Yi Zhu L. Hong AI4TS 149 35 0 11 May 2020
Physics-informed learning of governing equations from scarce data Zhao Chen Yang Liu Hao Sun PINN AI4CE 117 400 0 05 May 2020