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We introduce Weak-PDE-LEARN, a Partial Differential Equation (PDE) discovery algorithm that can identify non-linear PDEs from noisy, limited measurements of their solutions.
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“DeepMoD: Deep learning for Model Discovery in noisy data”
Gert-Jan Both, Subham Choudhury, Pierre Sens and Remy Kusters · 2021
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“Physics-informed learning of governing equations from scarce data”
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“Learning partial differential equations via data discovery and sparse optimization”
Hayden Schaeffer · 2017
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Clare Abreu, Jonathan Friedman, Vilhelm Woltz and Jeff Gore · 2019
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“Data-driven discovery of free-form governing differential equations”
Steven Atkinson et al · 2019
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“Data-driven discovery of PDEs in complex datasets”
Jens Berg and Kaj Nyström · 2019
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Zhao Chen, Yang Liu and Hao Sun · 2021
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“DeepGreen: deep learning of Green’s functions for nonlinear boundary value problems”
Craig Gin, Daniel Shea, Steven Brunton and J Kutz · 2021
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“Weak SINDy for partial differential equations”
Daniel Messenger and David Bortz · 2021
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“Data-driven discovery of Green’s functions with human-understandable deep learning”
Nicolas Boullé, Christopher Earls and Alex Townsend · 2022
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“Partial differential equations”
Lawrence Evans · 2022
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“Asymptotic consistency of the WSINDy algorithm in the limit of continuum data”
Daniel Messenger and David Bortz · 2022
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“PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data”
Robert Stephany and Christopher Earls · 2022
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“PDE-READ: Human-readable partial differential equation discovery using deep learning”
Robert Stephany and Christopher Earls · 2022
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“Learning the Delay Using Neural Delay Differential Equations”
Maria Oprea et al · 2023
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