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This video discusses the fourth stage of the machine learning process: (4) designing a loss function to assess the performance of the model. There are opportunities to incorporate physics into this stage of the process, such as adding regularization terms to promote sparsity or extra loss functions to ensure that a partial differential equation is satisfied, as in PINNs.
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
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00:00 Intro
00:55 Case Study: Fluid Velocity & Navier-Stokes
05:56 Case Study: Incompressible Flows & Poisson
07:46 Case Study: Lagrangian Neural Networks & Euler-Lagrange
09:38 Sparse Loss and the L1 Norm
12:51 Case Study: SINDy + AutoEncoder
15:41 SINDy and Loss Regularization
17:59 Parsimonious Modeling
20:16 Equivariant Loss
21:59 Outro