Long short-term memory (LSTM) networks allow to exhibit temporal dynamic behavior with feedback connections and seem a natural choice for learning sequences of 3D meshes. We introduce an approach for dynamic mesh representations as used for numerical simulations of car crashes. To bypass the complication of using 3D meshes, we transform the surface mesh sequences into spectral descriptors that efficiently encode the shape. A two branch LSTM based network architecture is chosen to learn the representations and dynamics of the crash during the simulation. The architecture is based on unsupervised video prediction by an LSTM without any convolutional layer. It uses an encoder LSTM to map an input sequence into a fixed length vector representation. On this representation one decoder LSTM performs the reconstruction of the input sequence, while the other decoder LSTM predicts the future behavior by receiving initial steps of the sequence as seed. The spatio-temporal error behavior of the model is analysed to study how well the model can extrapolate the learned spectral descriptors into the future, that is, how well it has learned to represent the underlying dynamical structural mechanics. Considering that only a few training examples are available, which is the typical case for numerical simulations, the network performs very well.
Summary.
Long short-term memory (LSTM) networks are trained to learn the underlying dynamics of 3D structures -- An encoder LSTM encodes a sequence of 3D mesh representations and two decoder LSTMs reconstruct the input itself and predict the future structural geometry. Such structural deformation sequences are collected from a simulation and used to train and evaluate the model. 
Strengths.
1. Deep neural network architecture is applied to learn a 3D mesh structure deformation. Its approach looks reasonable to me -- (i) a high-level (user-specified) feature is extracted to represent 3D structural mesh, and (ii) LSTMs are trained in this feature space. 
2. The paper shows that LSTM can learn the underlying dynamics of 3D structural deformation (but since it does not provide any comparison with other work, I cannot determine the prediction error is within a reasonable bound for this task).
Weaknesses.
1. Weak technical and theoretical novelty.
An existing LSTM-based seq-to-seq style architecture is simply chosen and applied to the 3D mesh reconstruction and prediction task. LSTM networks have been successfully applied to various tasks, thus it would be less impactful even if a paper shows LSTM works well in the specific task. 
2. Missing comparison with existing work.
(As far as I know) the task of predicting 3D structure deformation has long been studied and various simulation and analysis tools have widely used in academia and industries. The paper, however, does not thoroughly compare or cite any prior work in this area. As a reader from a different area, I would like to see more thorough analysis to support (i) what are the main bottlenecks of the conventional approaches, (ii) how the proposed data-driven approach can help to address these issues, and (iii) how the proposed approach can further extend.
For example, the paper mentions (as the main reason to use the deep neural network) that “Since one simulation run takes a couple of hours on a compute cluster, running a large number of simulation is not feasible” (in the 1st paragraph of Introduction). Can the authors provide an analysis of flops (floating-point operations per second)?
3. Simulated data for training. 
205 samples are collected from a simulation to train and validate the proposed model and half an hour has taken to train this model. Judging from the common practices, does this imply the underlying dynamics function (of a simulation) is trivial to learn? 
Also, only two parts (i.e. the left and right structural beam) of the whole 3D structure are analyzed. This implies that relations between any other parts are not considered and only a sole part is individually analyzed.
Minor concerns.
- Typo (In Figure 1 caption, ‘undo’).
- “Due to the encoder having 1000 layers”?
- Labels are needed in Figures.