Abstract: One embodiment of the present invention sets forth a technique for generating actuation values based on a target shape such that the actuation values cause a simulator to output a simulated soft body that matches the target shape. The technique includes inputting a latent code that represents a target shape and a point on a geometric mesh into a first machine learning model. The technique further includes generating, via execution of the first machine learning model, one or more simulator control values that specify a deformation of the geometric mesh, where each of the simulator control values is based on the latent code and corresponds to the input point, and generating, via execution of the simulator, a simulated soft body based on the one or more simulator control values and the geometric mesh. The technique further includes causing the simulated soft body to be outputted to a computing device.

Abstract: The present invention concerns a method of sampling a test signal. The method comprises: acquiring training signals sampled at a plurality of sampling locations; running an optimization procedure for determining an index set of n indices, representing a subset of the sampling locations, that maximize a function, over the training signals, of a quality parameter representing how well a given training signal is represented by the n indices; and sampling the test signal at the sampling locations represented by the n indices.

Abstract: The present invention concerns a method of sampling a test signal. The method comprises: acquiring (21) training signals sampled at a plurality of sampling locations; running (23) an optimization procedure for determining an index set of n indices, representing a subset of the sampling locations, that maximize a function, over the training signals, of a quality parameter representing how well a given training signal is represented by the n indices; and sampling (25) the test signal at the sampling locations represented by the n indices.