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Workshop: Machine Learning for Engineering Modeling, Simulation and Design

Differentiable Implicit Layers

Andreas Look · Simona Doneva · Melih Kandemir · Rainer Gemulla · Jan Peters


In this paper, we introduce an efficient backpropagation scheme for non-constrained implicit functions. These functions are parametrized by a set of learnable weights and may optionally depend on some input; making them perfectly suitable as learnable layer in a neural network. We demonstrate our scheme on different applications: (i) neural ODEs with the implicit Euler method, and (ii) system identification in model predictive control.

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