Lejeune et al., 2023 - Google Patents
Data driven discovery of systems of ordinary differential equations using nonconvex multitask learningLejeune et al., 2023
View HTML- Document ID
- 14586363303781139500
- Author
- Lejeune C
- Mothe J
- Soubki A
- Teste O
- Publication year
- Publication venue
- Machine Learning
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Snippet
In physical sciences, dynamic systems are modeled using their parameters within governing equations that often form a system of ordinary differential equations (SODE). This system consists of multiple equations, each of which relates the time derivative of a single …
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- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
- G06K9/6269—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on the distance between the decision surface and training patterns lying on the boundary of the class cluster, e.g. support vector machines
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
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- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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- G—PHYSICS
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- G06N3/02—Computer systems based on biological models using neural network models
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- G06K9/6279—Classification techniques relating to the number of classes
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- G—PHYSICS
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- G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
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