deep-learning-for-indentation
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lululxvi
Extraction of mechanical properties of materials through deep learning from instrumented indentation
Hello professor, thanks for your kindness and your work. I'm a student following your code work, and I faced some issues while operating mfnn code. I want to ask you...
Hello Professor, I am trying to present the results of Figure S1 A from your paper, specifically focusing on the values of E*. I am using DeepXDE version 1.1.2 with...
Hello, I was trying to execute your code, and I'm facing an issue with a certail function call. the deepdxe has been imported as dde, and the function in discussion...
Hi, MFNN is built based on low-fidelity data and high-fidelity data. I'm writing to ask what's the size of test dataset using high-fidelity data for the purpose of predictive accuracy....
Hello! First of all, thank you for publishing the code and the data for this paper. There is an issue using "nn.py". If I run the code without choosing anything...
Hello! Could you possibly add to the README instructions on how to replicate the results in your PNAS paper? Also, `fit_n.py` loads several `.dat` files that I don't see in...
Hello Prof. Lu! I am trying to replicate the results in Fig S1 A for which I am running the validation_model("sigma_y", 10) varying the train size 10 to 20, 30,...
ESOEER
hello! when I run nn.py, a mistake take place. OSError: [Errno 22] Invalid argement: "E*.dat" how to get the E*.data document
Hello Lu, First of all many thanks for your efforts and for making deepxde available, it is really useful. I am trying to reimplement the multifidelity network alone in Pytorch....
Thanks for sharing this novel algorithm to identify the material properties. I still have a few questions about transfer learning: 1. For the function “validation_exp_cross_transfer”, it seems that only the...
