Its my first time working with neural networks and I have been given the task of predicting some values of a dataset and I could make good use of some help on deciding which is the smartest architecture for the task. I'm working with Keras using Tensorflow as backend.
I'm not going into details but basically I have performed lots of CFD simulations on similar but slightly different geometries to obtain a stress value on the surface of the geometries. All the geometries have the same conectivity and number of nodes and I have the stress value for each of those nodes.
Simply put, I have an input matrix of [2500,3,300] where 2500 is the amount of nodes in each geometry, 3 represents the x,y,z coordinates in space of each node on the mesh and 300 is the total number of geometries I have. For the stress I have an output matrix of [2500,300] where 2500 is the value of stress for each node and 300 once again corresponds to the number of instances. I would like to train some kind of neural network so I can predict the stress values given the geometry.
I have been basing my architecture on the following paper but I can't not make use of the part in which the convolutional networks are employed. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5805990/
The simplest approach I can think of is a fully connected network but I struggle to figure out the layer architecture to relate the 3D matrix of the geometry to the 2D of the output stress matrix with my scarce knowledge of the subject.
Any suggestiong is more than welcomed. Thanks for your help!!!
Since I have been working with stress values prediction using DL, I would like to recommend you to work with CNN models which you have filters due to intelligent learning capacity even correlation between parameters your suppose to following. Nevertheless RNN and its promised version like LSTM & GRU have good performance on sufficient data if you have. Unfortunately I can't address you to paper because that this issue is still under study!
Another point I can turn out is reshape of your data is kind of important when you feed to NN models especially when you are dealing with time-series data.
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There is an adjacent matrix dataset that is based on time series. I would like to know if it is possible to build a neural network model to predict tn time point's matrix by using the previous time-series data. In my opinion, traditional models such as CNN may not fit for the sparse matrix graph.
Maybe you should give a look at Graph Neural Networks (specialy Spatial-Temporal Graph Networks). They use temporal information about graphs and its adjacency matrix to predict future nodes states, such values in the next-step.
You can read this survey paper as a start point and follow its cited works therefore.
I’m relatively new to the topic of machine learning, so naturally I have a couple of issues that I hope you can help me with or lead me in the right direction. I had a project before, during which we collected data of people walking normally and also with a stone in their shoe. We measured Acceleration and also with a gyroscope sensor. Based on this data I build a neural network that can classify the signals into normal or impaired walking. So two possible outputs.
Now my idea is this: I want to, using the same data, build a network that can predict the weights of the participants (it was also recorded).
Based on this my three questions:
- What kind of network structure is most suitable for such a task? (Dense, CNN, LSTM,…)
- Before the network basically had two options to answer from (normal or impaired walking) but now I have a continuous range of answers… How can this be approached?
- How can I make sure the network initializes with a sensible prediction?
I hope all the questions make sense. Any help will be much appreciated!
You can use the NNa architecture you prefer:
If you work with sequences use 1d convolutionals or RNNs.
As you are dealing with a regression problem you have to have a single neuron as output without activation function.
Take a.look here to learn to solve a regression problem with RNNs
I have a dataset, where X is three dimensional matrix with 760 (different id/individuals) x 300000 (electrophysiological time series data) x 15 (number of different channels). I have a continuous, numerical y which is unique to each of the individuals (N=760). I have to predict y from X (obviously...).
I'd like to use deep neural networks for this purpose, but I kind of lost in choosing the right model.
RNN/LSTM could be good, but it's just for forecasting the time series itself and not regression. I'm not sure whether a convolutional neural net could detect changes in time (reshaping X into a 760x4500000 matrix).
Could you suggest some valid approaches for this?
You can opt for either CNNs or LSTMs.
CNNs can actually be used for time series analysis and may provide better results than expected.
I provide here below an example which illustrates how convolution can be applied on time series.
The alternative is to use LSTMs/GRUs. I do not add here a description since, as compared to 1D CNNs, they are much better described on the internet.
Two things to note here are:
That both(CNNs and LSTMs) can be used for classification purposes.
Using neural networks would be an optimal choice due to having multi-variate regression.
I have a dataframe containing the coordinates of millions of particles which I want to use to train a Neural network. These particles build individual clusters which are already identified and labeled; meaning that every particle is already assigned to its correct cluster (this assignment is done by a density estimation but for my purpose not that relevant).
the challenge is now to build a network which does this clustering after learning from the huge data. there are also a few more features in the dataframe like clustersize, amount of particles in a cluster etc.
since this is not a classification problem but more a identification of clusters-challenge what kind of neural network should i use? I have also problems to build this network: for example a CNN which classifies wheather there is a dog or cat in the picture, the output is obviously binary. so also the last layer just consists of two outputs which represent the probability for being 1 or 0. But how can I implement the last layer when I want to identify clusters?
during my research I heard about self organizing maps. would these networks do the job?
thank you
If you want to treat clustering as a classification problem, then you can try to train the network to predict whether two points belong to the same clusters or to different clusters.
This does not ultimately solve your problems, though - to cluster the data, this labeling needs to be transitive (which it likely will not be) and you have to label n² pairs, which is expensive.
Furthermore, because your clustering is density-based, your network may need to know about further data points to judge which ones should be connected...
These particles build individual clusters which are already identified
and labeled; meaning that every particle is already assigned to its
correct cluster (this assignment is done by a density estimation but
for my purpose not that relevant).
the challenge is now to build a network which does this clustering
after learning from the huge data.
Sounds pretty much like a classification problem to me. Images themselves can build clusters in their image space (e.g. a vector space of dimension width * height * RGB).
since this is not a classification problem but more a identification
of clusters-challenge what kind of neural network should i use?
You have data of coordinates, you have labels. Start with a simple fully connected single/multi-layer-perceptron i.e. vanilla NN, with as many outputs as number of clusters and softmax-activation function.
There are tons of blogs and tutorials for Deep Learning libraries like keras out there in the internet.
I am making a project in which I have to predict a plane trajectory.
I have 2 types of trajectory, the first one is the planned, and the second one is the real one that I recovered after the end of the flight.
The two trajectories are (x,y) points on a map and I want to predict the real one with the planned one.
What kind of model do you use? I heard about multivariate regression or recurrent neural network but I am not sure about both, I think multivariate is not appropriate and rnn include time as parameter and I would not want to use it first.
Do you have any ideas?
Thank you
You could try either training single-target multiple regression models, and predict the x and y variables independently. The other way to go about is to use multi-target regression-based methods. The most commonly used method using Predictive Clustering trees. You can read about various methods from https://towardsdatascience.com/regression-models-with-multiple-target-variables-8baa75aacd to start with. I hope it is somewhat helpful. :)