PBDL model for 物理感知神经网络流固耦合计算加速方法研究-王兆坤 #1137
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| def main(OUTPUT_DIR): | ||
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| for i, (x1_batch, x2_batch, y_batch) in enumerate(train_loader): | ||
| x1_batch, x2_batch, y_batch = ( | ||
| x1_batch.to(device), | ||
| x2_batch.to(device), | ||
| y_batch.to(device), | ||
| ) | ||
| break | ||
| for i, (x1_test, x2_test, y_test) in enumerate(test_loader): | ||
| x1_test, x2_test, y_test = ( | ||
| x1_test.to(device), | ||
| x2_test.to(device), | ||
| y_test.to(device), | ||
| ) | ||
| break |
| np.linalg.norm(test_prediction - test_target) | ||
| / np.linalg.norm(test_target) | ||
| ) | ||
| with open(f"{OUTPUT_DIR}/TestingLoss_L2.dat", "a") as file2: |
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文件路径请使用os.path.join来拼接
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请转换成.pdparams格式的文件
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- 这个文件和checkpoint.pth有什么区别，如果是重复的文件，可以删除，否则请转换成
.pdparams格式的文件
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Add an example about PBDL model used in '物理感知神经网络流固耦合计算加速方法研究'
Describe
This is the physics-based deep learning model to predict body force used in immerse boundary method. It can accelerate the traditional LBM calculation by this rapid predicted body force.