We invite interested scholars to work with us on developing new systems that apply machine learning to achieve accurate results in dynamic mechanical measurements. Machine learning provides a powerful means for performing deconvolution on sensor outputs to provide accurate measurements of dynamic inputs. Trained neural networks can provide an accurate system response in the absence of complete knowledge of the measurement system and can accomodate nonlinear behavior often important in real physical systems. This work will focus on the development of physics-constrained neural network representations of dynamic mechanical measurement systems, training the constrained networks using known calilbration inputs, and applying the trained networks to achieve accurate measurements of arbitrary-waveform unknown inputs. The developed neural networks will also serve as physics discovery tools, as they provide accurate representations of the heretofore poorly understood physical systems.