zabaras / transformer-physx Goto Github PK
View Code? Open in Web Editor NEWTransformers for modeling physical systems
Home Page: https://zabaras.github.io/transformer-physx/
License: MIT License
Transformers for modeling physical systems
Home Page: https://zabaras.github.io/transformer-physx/
License: MIT License
Maybe mask0 should be a tensor not a bool value. So we need to delete "is True".
Hi @NickGeneva! Thanks for the nice work!
I'm working on re-implementing the relative mean-squared error (MSE) metric as described in your paper. However, I'm having difficulty finding a clear, universally accepted definition of relative MSE in this context. Could you please provide the exact formula you used to calculate these values?
Specifically, is the relative MSE calculated as:
Thanks!
Hello!
Thank you for the interesting paper.
Could you please share the data you used for the Lorenz benchmark?
Thank you
What should be the input to the embedding and transformer colab? How are the HDF5 files structured? What is the meaning of the following naming in the hdf5 files ['0', '1', '10', '100', '1000', '1001', '1002', '1003', '1004', '1005', '1006', '1007', '1008', '1009', '101', '1010'... ?
Dear authors:
Thanks for your interesting and inspirational work! But I think there may be a minor mistake in the provided codes. In the file embedding_lorenz.py, the following codes are included
# Test accuracy of one time-step
for i in range(xInput.size(1)):
xInput0 = xInput[:,i].to(device)
g0 = self.embedding_model.embed(xInput0)
yPred0 = self.embedding_model.recover(g0)
yPred[:,i] = yPred0.squeeze().detach()
test_loss = mseLoss(yTarget, yPred)
I think it should be
# Test accuracy of one time-step
for i in range(xInput.size(1)):
xInput0 = xInput[:,i].to(device)
g0 = self.embedding_model.embed(xInput0)
g0 = self.embedding_model.koopmanOperation(g0)
yPred0 = self.embedding_model.recover(g0)
yPred[:,i] = yPred0.squeeze().detach()
test_loss = mseLoss(yTarget, yPred)
for its practical effect.
Hi, i have an issue with the input of ConvLstms yu presented it in the benchamrk for the paper. Wht is th input is it a mask or initial conditions. Thanks a lot.
Hello!
Thanks for your exciting work!
I'm confused about the result of the Lorenz system. The paper shows that the Lorenz system's prediction results are perfect, but I can't reproduce it based on the currently provided code. Moreover, the run results provided in Colab are also not great enough (states error stay 18). I really want to know what caused this.
Thank you very much!
Hello,
Thank you very much for this amazing work.
I am trying to understand a bit better differentiable physics for fluid dynamics and this would help me a lot.
However when I am trying to run your code there is an error on cell 14 that I was unable to solve. Maybe due to an update of HfArgumentParser? Everything ran well on the previous cells (except for a small typo on cell 5 where there were some spaces after the '%' sign but that's straight forward to fix)
Do you have an idea on how to fix it and run your code?
Thank you very much
Hi, I got the error where these functions can't be found.
from trphysx.data_utils import AutoDataset, AutoPredictionDataset
from trphysx.data_utils.data_utils import EvalDataCollator
from trphysx.data_utils.dataset_lorenz import LorenzPredictDataset
is there any updated code?
Please advice
thanks
Hi @NickGeneva very nice work!
do you plan to release the embedding modules (from ..embedding.embedding_model import EmbeddingModel) and one example of test?
thanks!
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.