Frequently Asked Questions

This section provides quick responses to typical and frequently asked questions regarding physicsAI.

Data and Formats

What file formats are supported for training data?
The file reading technology is common throughout the ecosystem, for example HyperView. For more information, see Coverage.
Are transient simulations supported?
Yes, both transient and static simulation are supported.
How much data is needed?
The number of result files required to get good quality predictions will vary between projects, and sufficient quality is itself subjective. Some application problems may require only a handful of results, while others require dozens or even hundreds. As a general guideline, it is recommended to train with at least 10 results before assessing tests of predictive quality.
Do meshes need to have the same number of element/nodes?
No, the meshes do not require equivalent mesh. The meshes do not even need to be topologically equivalent.
How much can the design data vary?
There is no specific limit on the allowed variation in the training data. But it is informative to keep two considerations in mind. First, the training data should be representative of the type of data on which predictions will be made. Second, datasets with higher variability require a correspondingly larger number of training examples to maintain quality. When predicting, the confidence score can be used to quantify how similar the design is to the training data.

Computing and Resources

Is a GPU required?
A CPU can be used to train a model, but it will be slower than a GPU.
What GPUs are supported?
To train with a GPU, you must install CUDA toolkit 11.8 and cuDNN 8.7. This requires a NVIDIA GPU of at least a Pascal microarchitecture, that has a compute capability > 6.0.
What effect does GPU and dataset size have on training times?
The quality of computing resources will affect training time. Better hardware can improve run times, but improvements from CPU to GPU can be substantial. Regardless, training time is linearly proportional to dataset size. See the table below for a representative example:
Hardware 10 Result Files 50 Result Files 100 Result Files
Laptop CPU 44 m 3 h 35 m 7 h 54 m
HPC CPU 34 m 2 h 46 m 5 h 10 m
GPU 3 m 16m 33 m
Can I train on an HPC?
Yes, more information is available here.

Accuracy and Quality

Is physicsAI accurate?
In general, the accuracy of a physicsAI model will improve with the amount of data, expressivity in the model (for example, width and depth), and allotted training time. However, practical considerations impose finite limits on these quantities. Assessing the quality of a trained model, for example by testing the model’s MAE metric against known values, is a standard step in the process of training models.
What is a good MAE?
MAE is the mean absolute error. MAE can be interpreted as an error measure of a prediction. For example, consider a model that predicts displacement with an MAE of 4 mm. This means any given prediction may be inaccurate, on average, by 4mm. This may be significant if the predicted displacement of engineering interest is only 5 mm, yet less consequential if a typical value is 500 mm.
What training settings should be used?
Every project is different. The default settings are good places to begin, but the best practice is to tune the settings to achieve a sufficiently high-quality model. The physicsAI workflows permit the repeated training of models on the same dataset to compare the outcomes across different settings. These experiments may provide empirical evidence that similar projects may achieve the best results with similar settings.
Can a physicsAI trained model replace a solver?
Yes and no. PhysicsAI models are designed to act as fast approximations of a solver, so in general, we do not expect solver-level accuracy. It is typically one to two orders of magnitude faster than a solver. This can be useful, even without achieving solver-level accuracy, because it allows you to rapidly analyze new design concepts. The final design should always be verified with a traditional solver. That said, physicsAI models can be trained to be quite accurate given the proper training data and settings.