What is recorded here?
Parameterizes a Lagrangian with a neural network and uses automatic derivatives and a velocity-Hessian inverse to predict accelerations from generalized coordinates.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Six original authors; affiliation stars are not shared-lead marks. Inherits Euler–Lagrange mechanics and earlier restricted DeLaN; not first neural Lagrangian method. Invertibility/regularity are needed for unambiguous dynamics; pseudoinverse is a practical response to singularity, not proof of a regular physical system. Cubic Hessian inversion and integration error remain; learned conservation is not universal true-energy accuracy.
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People and systems
Miles Cranmer; Sam Greydanus; Stephan Hoyer; Peter Battaglia; David Spergel; Shirley Ho
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Lagrangian neural networks using differentiated Euler–Lagrange dynamics
Parameterizes a Lagrangian with a neural network and uses automatic derivatives and a velocity-Hessian inverse to predict accelerations from generalized coordinates.
Scientific scope
Six original authors; affiliation stars are not shared-lead marks. Inherits Euler–Lagrange mechanics and earlier restricted DeLaN; not first neural Lagrangian method. Invertibility/regularity are needed for unambiguous dynamics; pseudoinverse is a practical response to singularity, not proof of a regular physical system. Cubic Hessian inversion and integration error remain; learned conservation is not universal true-energy accuracy.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Miles Cranmer; Sam Greydanus; Stephan Hoyer; Peter Battaglia; David Spergel; Shirley Ho
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.