Data-driven discovery of partial differential equations

Samuel Rudy, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz · Science Advances · 2017 · physics · 10.1126/sciadv.1602614 · record rudy2017-data-driven-discovery-of-partial-d
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Other fields define these differently the same word, defined another way in another discipline
noise 2 fields

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 4 concept tags on this paper, all 4 are grounded in a quoted sentence. All carry the tagger's gloss of what the term means in this paper, which is not something the article can license.
overcomplete library sparse regression Pareto analysis parsimonious model noise initial data system-or-tool-building
Method, as described: sparse regression over a library of candidate nonlinear and derivative terms, validated by rediscovering known physical laws

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relation to other workclaim_relationshow this paper stands to another: agrees, contradicts, extends, qualifies
claim typeclaim_typethe shape of the claim: existence, mechanism, method, critique, prediction, null
conceptconceptsan idea the paper works with, quoted where the paper uses it
content tagcontent_tagsa finding, definition, quantity, statistic or variable named in the paper
contributioncontributionwhat the paper claims to add
controlcontrolsan experiment run to eliminate a rival explanation
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
object of studyobject_of_studywhat the paper is about — the thing it investigates
phenomenonphenomenonthe effect or regularity under study
research questionresearch_questionthe question the paper sets itself
sensesensesa distinct use of a term, quoted where the paper fixes it

The paper

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Every tag on this page

claim_relations 2 · claim_type 1 · concepts 4 · content_tags 10 · contribution 1 · controls 3 · limitations 3 · mechanisms 2 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 2

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facettagfieldconf.whose words
object_of_studythe governing partial differential equations of a spatiotemporal system, recovered from time series measurements
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object_of_studythe paper's words
research_questionwhether the governing equations of a system can be discovered from data where first-principles derivation is intractable
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research_questionthe paper's words
contributiona sparse regression method that selects the terms of a PDE from a large candidate library, with parsimony enforced by Pareto analysis and the library's sufficiency written down as an assumption
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contributionthe paper's words
claim_typemethod
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claim_typethe paper's words
phenomenonspatiotemporal dynamics whose governing equations have eluded analytic derivation
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phenomenonthe paper's words
methodsystem-or-tool-building
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methodthe paper's words
mechanismssparsity-promoting regression selects the few library terms that represent the data, which is what BYPASSES a combinatorially large search over all candidate models
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mechanisms[0]0.95the tagger's words
mechanismsparsimony is enforced by Pareto analysis - accuracy against complexity - which is the same device the evolutionary and sparse symbolic-regression methods use to avoid overfitting
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mechanisms[1]0.95the tagger's words
limitationsTHE METHOD CAN ONLY DISCOVER WHAT IS ALREADY IN ITS LIBRARY, and the paper states the sufficiency of the library as an ASSUMPTION: `if we assume that Theta is an overcomplete library, meaning Theta has a sufficiently rich column space that the dynamics will be in its range`
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limitations[0]0.95the tagger's words
limitationsTHE NAMED FAILURE IS RIGHT TERMS AND WRONG NUMBERS: on Kuramoto-Sivashinsky the correct terms are identified and there is substantial coefficient error under noise, which the paper calls a limitation of the method rather than of the example
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limitations[1]0.95the tagger's words
limitationsthe validation is REDISCOVERY - a method for finding equations nobody knows, tested on Navier-Stokes, the quantum harmonic oscillator and the diffusion equation
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limitations[2]0.95the tagger's words
controlsthe method is run on systems whose governing equations are already known - Navier-Stokes, the quantum harmonic oscillator, the diffusion equation - and asked to REDISCOVER them
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eliminates: a discovery procedure that produces a plausible equation for any data, since on these systems the right answer is known in advance and the method must return it · outcome: held
controls[0]0.95the tagger's words
controlsGaussian noise is added to every example and the method is re-run, and the data is also subsampled spatially
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eliminates: a recovery that depends on clean, densely sampled data - noise is stated to significantly complicate both differentiation and library building · outcome: partial
controls[1]0.95the tagger's words
controlsmultiple time series taken with DIFFERENT INITIAL DATA are used where terms are potentially nonunique
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eliminates: two different equations that fit one trajectory equally well being indistinguishable - a linear wave equation against Korteweg-de Vries · outcome: held
controls[2]0.95the tagger's words
conceptsovercomplete library
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concepts[0]0.95the paper's words
conceptssparse regression
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concepts[1]0.95the paper's words
conceptsPareto analysis
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concepts[2]0.95the paper's words
conceptsparsimonious model
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concepts[3]0.95the paper's words
sensesovercomplete library - AS DEFINED HERE: a candidate set whose column space is rich enough that the true dynamics lie in its range. The word `assume` is the paper's, and everything the method can find is inside it
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senses[0]0.95the tagger's words
sensesparsimonious model - the point on the accuracy-against-complexity Pareto front, not the model with fewest terms and not the most accurate one
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senses[1]0.95the tagger's words
claim_relationsextends → the sparse and evolutionary symbolic-regression methods for discovering dynamical systems, carried from ODEs to partial differential equations
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claim_relations[0]0.95the tagger's words
claim_relationscritiques → first-principles derivation as the only route to a governing equation, on the ground that many complex systems have eluded it
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claim_relations[1]0.95the tagger's words
content_tagsthe method assumes an overcomplete library whose column space is rich enough to contain the dynamics
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content_tags.findings[0]0.95the tagger's words
content_tagson Kuramoto-Sivashinsky the CORRECT TERMS ARE IDENTIFIED and the COEFFICIENTS ARE SUBSTANTIALLY WRONG under noise - the paper separates the two failure modes and calls the second a limitation of the method
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content_tags.findings[1]0.95the tagger's words
content_tagsthe method distinguishes a linear wave equation from Korteweg-de Vries by using multiple time series with different initial data
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content_tags.findings[2]0.95the tagger's words
content_tagsevery example is re-identified after Gaussian noise is added, and after significant spatial subsampling
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content_tags.findings[3]0.95the tagger's words
content_tagsa candidate matrix with a sufficiently rich column space that the dynamics will be in its range - stated as an assumption, not established
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content_tags.definitions[0]0.95the tagger's words
content_tagsthe model selected by Pareto analysis, balancing model complexity against regression accuracy
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content_tags.definitions[1]0.95the tagger's words
content_tagscanonical problems named in the abstract - Navier-Stokes, the quantum harmonic oscillator and the diffusion equation - as the validation set
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content_tags.quantities[0]0.95the tagger's words
content_tagssparse regression
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content_tags.statistics[0]0.95the tagger's words
content_tagsnoise
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also in: noise
content_tags.variables[0]0.95the tagger's words
content_tagsinitial data
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also in: initial data
content_tags.variables[1]0.95the tagger's words

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