Kathleen M. Champion
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View article: Discovering governing equations from partial measurements with deep delay autoencoders
Discovering governing equations from partial measurements with deep delay autoencoders Open
A central challenge in data-driven model discovery is the presence of hidden, or latent, variables that are not directly measured but are dynamically important. Takens’ theorem provides conditions for when it is possible to augment partial…
View article: Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders Open
A central challenge in data-driven model discovery is the presence of hidden, or latent, variables that are not directly measured but are dynamically important. Takens' theorem provides conditions for when it is possible to augment these p…
View article: dynamicslab/pysindy: Add cvxpy to requirements
dynamicslab/pysindy: Add cvxpy to requirements Open
This release simply moves cvxpy from an optional dependency to a required one. This package is needed to use the TrappingSR3 optimizer.
View article: dynamicslab/pysindy: Trapping SINDy
dynamicslab/pysindy: Trapping SINDy Open
This release introduces the TrappingSINDy optimizer, which "enables the identification of models that, by construction, only produce bounded trajectories." To use TrappingSINDy you will need to install the cvxpy package. Please see this pa…
View article: dynamicslab/pysindy: [Bug fix] Simulate with control inputs
dynamicslab/pysindy: [Bug fix] Simulate with control inputs Open
This release fixes a minor issue causing SINDy.simulate to fail when vectors of control inputs are passed in (see #94).
View article: A unified sparse optimization framework to learn parsimonious\n physics-informed models from data
A unified sparse optimization framework to learn parsimonious\n physics-informed models from data Open
Machine learning (ML) is redefining what is possible in data-intensive fields\nof science and engineering. However, applying ML to problems in the physical\nsciences comes with a unique set of challenges: scientists want physically\ninterp…