【Hackathon 8th No.13】Domino 论文复现 #1093

xiaoyewww · 2025-03-04T17:00:51Z

PR types

New Features

PR changes

Others

Describe

support domino

paddle-bot · 2025-03-04T17:00:55Z

Thanks for your contribution!

xiaoyewww · 2025-03-04T17:06:18Z

复现Domino有两个问题：

目前只复现了模型内容，另外还有前处理和后处理部分，需要的CPU配置和内存非常高，aistudio上无法跑通
官方没有提供预训练权重

luotao1 · 2025-03-05T12:02:40Z

请先提交 RFC 设计文档

wangguan1995 · 2025-03-10T11:27:52Z

不好意思这里任务描述有误，需要改为【推理】和【训练】

luotao1 · 2025-03-11T02:32:20Z

@wangguan1995 可以提PR修改下任务描述 https://github.com/PaddlePaddle/community/blob/master/hackathon/hackathon_8th/%E3%80%90Hackathon_8th%E3%80%91%E4%B8%AA%E4%BA%BA%E6%8C%91%E6%88%98%E8%B5%9B%E2%80%94%E5%A5%97%E4%BB%B6%E5%BC%80%E5%8F%91%E4%BB%BB%E5%8A%A1%E5%90%88%E9%9B%86.md#no13-domino-%E8%AE%BA%E6%96%87%E5%A4%8D%E7%8E%B0

xiaoyewww · 2025-03-15T15:49:56Z

@wangguan1995 目前模型已经能正常训练，精度通过Padiff验证通过。训练代码存在随机性，每个step前处理数据没法通过随机数种子固定。
目前还有需要验证的任务有：
（1）目前缺少数据集，仅通过一个样本进行训练，50个epoch loss正常下降
（2）推理代码已经适配，但前处理部分仍需进行点云处理，aistudio上无法处理，这部分需要继续验证

wangguan1995 · 2025-04-03T08:20:49Z

目前代码仓库存在大量相对路径
下载脚本目前有一些问题（aws自己的问题，先标记在文档里）
run_1作为验证训练的数据，10个epoch的torch对比日志贴在这里
需要做的是前向loss 1e-5级别的对齐

xiaoyewww · 2025-04-03T15:20:16Z

前10 个epoch日志：

torch

Device cuda:0, batch processed: 1, loss volume: 0.23092692             , loss surface: 0.06465874, loss integral: 0.00000000, loss surface area: 0.00293629
 Device cuda:0,  batch: 1, loss norm: 0.26472443
Device cuda:0 LOSS train 0.26472443 valid 0.19338508 Current lr 0.001Integral factor 0
Device cuda:0, Best val loss 0.05669114366173744, Time taken 118.63378143310547

Device cuda:0, epoch 1:
Device cuda:0, batch processed: 1, loss volume: 0.17430124             , loss surface: 0.03637335, loss integral: 0.00000000, loss surface area: 0.00179438
 Device cuda:0,  batch: 1, loss norm: 0.19338511
Device cuda:0 LOSS train 0.19338511 valid 0.11204524 Current lr 0.001Integral factor 0
Device cuda:0, Best val loss 0.05669114366173744, Time taken 114.84539103507996

Device cuda:0, epoch 2:
Device cuda:0, batch processed: 1, loss volume: 0.09825166             , loss surface: 0.02525518, loss integral: 0.00000000, loss surface area: 0.00233155
 Device cuda:0,  batch: 1, loss norm: 0.11204502
Device cuda:0 LOSS train 0.11204502 valid 0.10328176 Current lr 0.001Integral factor 0
Device cuda:0, Best val loss 0.05669114366173744, Time taken 115.87338018417358

Device cuda:0, epoch 3:
Device cuda:0, batch processed: 1, loss volume: 0.09899043             , loss surface: 0.00795730, loss integral: 0.00000000, loss surface area: 0.00062173
 Device cuda:0,  batch: 1, loss norm: 0.10327994
Device cuda:0 LOSS train 0.10327994 valid 0.05417285 Current lr 0.001Integral factor 0
Device cuda:0, Best val loss 0.05417285114526749, Time taken 116.46289348602295

Device cuda:0, epoch 4:
Device cuda:0, batch processed: 1, loss volume: 0.04868028             , loss surface: 0.01017947, loss integral: 0.00000000, loss surface area: 0.00080577
 Device cuda:0,  batch: 1, loss norm: 0.05417290
Device cuda:0 LOSS train 0.05417290 valid 0.08227389 Current lr 0.001Integral factor 0
Device cuda:0, Best val loss 0.05417285114526749, Time taken 115.91964483261108

Device cuda:0, epoch 5:
Device cuda:0, batch processed: 1, loss volume: 0.07733711             , loss surface: 0.00911645, loss integral: 0.00000000, loss surface area: 0.00075788
 Device cuda:0,  batch: 1, loss norm: 0.08227427
Device cuda:0 LOSS train 0.08227427 valid 0.08577856 Current lr 0.001Integral factor 0
Device cuda:0, Best val loss 0.05417285114526749, Time taken 115.41759729385376

Device cuda:0, epoch 6:
Device cuda:0, batch processed: 1, loss volume: 0.08035985             , loss surface: 0.01013019, loss integral: 0.00000000, loss surface area: 0.00070755
 Device cuda:0,  batch: 1, loss norm: 0.08577872
Device cuda:0 LOSS train 0.08577872 valid 0.06831404 Current lr 0.001Integral factor 0
Device cuda:0, Best val loss 0.05417285114526749, Time taken 115.05477333068848

Device cuda:0, epoch 7:
Device cuda:0, batch processed: 1, loss volume: 0.06417362             , loss surface: 0.00766032, loss integral: 0.00000000, loss surface area: 0.00062155
 Device cuda:0,  batch: 1, loss norm: 0.06831456
Device cuda:0 LOSS train 0.06831456 valid 0.04400067 Current lr 0.001Integral factor 0
Device cuda:0, Best val loss 0.044000666588544846, Time taken 116.03830194473267

Device cuda:0, epoch 8:
Device cuda:0, batch processed: 1, loss volume: 0.04010706             , loss surface: 0.00724047, loss integral: 0.00000000, loss surface area: 0.00054595
 Device cuda:0,  batch: 1, loss norm: 0.04400026
Device cuda:0 LOSS train 0.04400026 valid 0.04530785 Current lr 0.001Integral factor 0
Device cuda:0, Best val loss 0.044000666588544846, Time taken 115.86109900474548

Device cuda:0, epoch 9:
Device cuda:0, batch processed: 1, loss volume: 0.04175998             , loss surface: 0.00659478, loss integral: 0.00000000, loss surface area: 0.00050289
 Device cuda:0,  batch: 1, loss norm: 0.04530882
Device cuda:0 LOSS train 0.04530882 valid 0.06185470 Current lr 0.001Integral factor 0
Device cuda:0, Best val loss 0.044000666588544846, Time taken 115.73935866355896

Device cuda:0, epoch 10:
Device cuda:0, batch processed: 1, loss volume: 0.05804047             , loss surface: 0.00707586, loss integral: 0.00000000, loss surface area: 0.00055142
 Device cuda:0,  batch: 1, loss norm: 0.06185411
Device cuda:0 LOSS train 0.06185411 valid 0.04006197 Current lr 0.001Integral factor 0
Device cuda:0, Best val loss 0.04006196931004524, Time taken 116.31600141525269

paddle:

Device gpu:0, batch processed: 1, loss volume: 0.23092692             , loss surface: 0.06465873, loss integral: 0.00000000, loss surface area: 0.00293629
 Device gpu:0,  batch: 1, loss norm: 0.26472443
Loss/train: 0.2647244334220886/1
Device gpu:0 LOSS train 0.26472443 valid 0.19338842 Current lr 0.001Integral factor 0
Device gpu:0, Best val loss 0.05531581491231918, Time taken 118.70474576950073

Device gpu:0, epoch 1:
Device gpu:0, batch processed: 1, loss volume: 0.17430471             , loss surface: 0.03637305, loss integral: 0.00000000, loss surface area: 0.00179433
 Device gpu:0,  batch: 1, loss norm: 0.19338840
Loss/train: 0.19338840246200562/2
Device gpu:0 LOSS train 0.19338840 valid 0.11218615 Current lr 0.001Integral factor 0
Device gpu:0, Best val loss 0.05531581491231918, Time taken 115.69161486625671

Device gpu:0, epoch 2:
Device gpu:0, batch processed: 1, loss volume: 0.09839579             , loss surface: 0.02524963, loss integral: 0.00000000, loss surface area: 0.00233097
 Device gpu:0,  batch: 1, loss norm: 0.11218609
Loss/train: 0.11218608915805817/3
Device gpu:0 LOSS train 0.11218609 valid 0.10281664 Current lr 0.001Integral factor 0
Device gpu:0, Best val loss 0.05531581491231918, Time taken 115.92429780960083

Device gpu:0, epoch 3:
Device gpu:0, batch processed: 1, loss volume: 0.09853126             , loss surface: 0.00795383, loss integral: 0.00000000, loss surface area: 0.00062137
 Device gpu:0,  batch: 1, loss norm: 0.10281885
Loss/train: 0.10281885415315628/4
Device gpu:0 LOSS train 0.10281885 valid 0.05420898 Current lr 0.001Integral factor 0

Device gpu:0, Best val loss 0.05420897901058197, Time taken 116.74323606491089

Device gpu:0, epoch 4:
Device gpu:0, batch processed: 1, loss volume: 0.04871760             , loss surface: 0.01017731, loss integral: 0.00000000, loss surface area: 0.00080539
 Device gpu:0,  batch: 1, loss norm: 0.05420895
Loss/train: 0.05420895293354988/5
Device gpu:0 LOSS train 0.05420895 valid 0.08210348 Current lr 0.001Integral factor 0
Device gpu:0, Best val loss 0.05420897901058197, Time taken 115.5925304889679

Device gpu:0, epoch 5:
Device gpu:0, batch processed: 1, loss volume: 0.07717736             , loss surface: 0.00909785, loss integral: 0.00000000, loss surface area: 0.00075586
 Device gpu:0,  batch: 1, loss norm: 0.08210421
Loss/train: 0.08210421353578568/6
Device gpu:0 LOSS train 0.08210421 valid 0.08545748 Current lr 0.001Integral factor 0
Device gpu:0, Best val loss 0.05420897901058197, Time taken 115.61996293067932

Device gpu:0, epoch 6:
Device gpu:0, batch processed: 1, loss volume: 0.08003174             , loss surface: 0.01014488, loss integral: 0.00000000, loss surface area: 0.00070565
 Device gpu:0,  batch: 1, loss norm: 0.08545700
Loss/train: 0.08545700460672379/7
Device gpu:0 LOSS train 0.08545700 valid 0.06784783 Current lr 0.001Integral factor 0
Device gpu:0, Best val loss 0.05420897901058197, Time taken 116.20984172821045

Device gpu:0, epoch 7:
Device gpu:0, batch processed: 1, loss volume: 0.06372426             , loss surface: 0.00762877, loss integral: 0.00000000, loss surface area: 0.00061806
 Device gpu:0,  batch: 1, loss norm: 0.06784768
Loss/train: 0.06784767657518387/8
Device gpu:0 LOSS train 0.06784768 valid 0.04360897 Current lr 0.001Integral factor 0
Device gpu:0, Best val loss 0.04360896721482277, Time taken 116.99345993995667

Device gpu:0, epoch 8:
Device gpu:0, batch processed: 1, loss volume: 0.03972780             , loss surface: 0.00721701, loss integral: 0.00000000, loss surface area: 0.00054254
 Device gpu:0,  batch: 1, loss norm: 0.04360757
Loss/train: 0.043607573956251144/9
Device gpu:0 LOSS train 0.04360757 valid 0.04554129 Current lr 0.001Integral factor 0
Device gpu:0, Best val loss 0.04360896721482277, Time taken 116.08153223991394

Device gpu:0, epoch 9:
Device gpu:0, batch processed: 1, loss volume: 0.04196901             , loss surface: 0.00663531, loss integral: 0.00000000, loss surface area: 0.00050686
 Device gpu:0,  batch: 1, loss norm: 0.04554009
Loss/train: 0.04554009437561035/10
Device gpu:0 LOSS train 0.04554009 valid 0.06155418 Current lr 0.001Integral factor 0
Device gpu:0, Best val loss 0.04360896721482277, Time taken 115.41889214515686

Device gpu:0, epoch 10:
Device gpu:0, batch processed: 1, loss volume: 0.05775697             , loss surface: 0.00704247, loss integral: 0.00000000, loss surface area: 0.00054853
 Device gpu:0,  batch: 1, loss norm: 0.06155247
Loss/train: 0.061552468687295914/11
Device gpu:0 LOSS train 0.06155247 valid 0.04058465 Current lr 0.001Integral factor 0
Device gpu:0, Best val loss 0.04058464989066124, Time taken 116.95455741882324

wangguan1995 · 2025-05-19T02:52:52Z

examples/domino/README.md

@@ -0,0 +1,43 @@
+# DoMINO: Decomposable Multi-scale Iterative Neural Operator for External Aerodynamics


ReadMe需要合入文档，可以参考：https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/development/#3

已修改，麻烦review

wangguan1995 · 2025-05-19T02:53:10Z

examples/domino/modulus/launch/logging/mlflow.py

+from typing import Literal
+from typing import Tuple
+
+import torch


需要清理相关内容

wangguan1995 · 2025-05-19T02:53:17Z

examples/domino/modulus/launch/logging/mlflow.py

+    time_string = start_time.strftime("%m/%d/%y %H:%M:%S")
+    client.set_tag(run.info.run_id, "date", time_string)
+    client.set_tag(run.info.run_id, "host", os.uname()[1])
+    if torch.cuda.is_available():


需要清理相关内容

wangguan1995 · 2025-05-19T02:53:21Z

examples/domino/modulus/launch/logging/utils.py

+
+from datetime import datetime
+
+import torch


需要清理相关内容

wangguan1995 · 2025-05-19T02:53:31Z

examples/domino/modulus/launch/utils/checkpoint.py

+    path : str
+        Path to training checkpoint
+    models : Union[paddle.nn.Layer, List[paddle.nn.Layer], None], optional
+        A single or list of PyTorch models, by default None


需要清理相关内容

wangguan1995 · 2025-05-19T02:54:06Z

examples/domino/README.md

+
+## 推理
+
+1. 修改`conf/config.yaml`路径, 原始配置文件参考[DoMINO](https://github.com/NVIDIA/modulus/blob/main/examples/cfd/external_aerodynamics/domino/src/conf/config.yaml)。


modulus相关内容代码需要进一步整理，使得文件夹的相关内容可以合入PaddleScience的arch文件夹

已整理合入arch文件，麻烦review

wangguan1995 · 2025-05-19T02:58:08Z

目前计划进行数据精度验证

5.19 ~ 5.21 进行数据处理
5.22 ~ 5.23 进行小数据集调试，并行调试
5.23 ~ 5.26 完成验证进行合入

wangguan1995

To fix

wangguan1995 · 2025-05-28T06:57:49Z

examples/domino/conf/config.yaml

@@ -0,0 +1,124 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.


Copy Right需要改为PaddlePaddle，并声明是对原始论文的复现

wangguan1995 · 2025-05-28T06:59:06Z

examples/domino/process_data.py

@@ -0,0 +1,109 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.


合入至 ppsci/data/process

wangguan1995 · 2025-05-28T06:59:42Z

examples/domino/requirements.txt

@@ -0,0 +1,6 @@
+hydra-core


合入至https://github.com/PaddlePaddle/PaddleScience/blob/develop/requirements.txt

wangguan1995 · 2025-05-28T07:01:02Z

ppsci/arch/physicsnemo/datapipes/cae/__init__.py

@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.


data pipe文件夹需要按照要求合入至ppsci/data/dataset模块

wangguan1995 · 2025-05-28T07:02:28Z

ppsci/arch/physicsnemo/distributed/manager.py

solver支持分布式并行，参考：https://github.com/PaddlePaddle/PaddleScience/blob/develop/ppsci/solver/train.py进行合并

这部分manager对代码影响不大，只用到了rank和world size，直接用paddle.dist.get_rank和get_world_size做了替换

wangguan1995 · 2025-05-28T07:02:50Z

ppsci/arch/physicsnemo/launch/utils/__init__.py

@@ -0,0 +1,18 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.


wangguan1995 · 2025-05-28T07:02:59Z

ppsci/arch/physicsnemo/launch/utils/checkpoint.py

@@ -0,0 +1,378 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.


wangguan1995 · 2025-05-28T07:04:21Z

ppsci/arch/physicsnemo/models/model.py

@@ -0,0 +1,1178 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.


需要将多个py文件合并为单个ppsci/arch/physicsnemo.py
参考：ppsci/arch/kan.py

wangguan1995 · 2025-05-28T07:06:07Z

ppsci/arch/physicsnemo/utils/sdf.py

@@ -0,0 +1,143 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.


需要合入至：ppsci/utils/ 模块

wangguan1995 · 2025-05-28T07:07:21Z

examples/domino/train.py

@@ -0,0 +1,946 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.


train 和 test需要合并

wangguan1995

to fix

wangguan1995 · 2025-06-03T03:51:47Z

docs/zh/examples/domino.md

+
+外部空气动力学涉及高雷诺数Navier-Stokes方程求解，传统CFD方法计算成本高昂。神经算子通过端到端映射提升了效率，但面临多尺度耦合建模与长期预测稳定性不足的挑战。Decomposable Multi-scale Iterative Neural Operator（Domino）提出可分解多尺度架构，通过分层特征解耦、迭代残差校正及参数独立编码，显著提升跨尺度流动建模精度与泛化能力。实验显示，其计算速度较CFD快2-3个量级，分离流预测精度较FNO等模型提升约40%，为飞行器设计等工程问题提供高效解决方案。
+
+## 2. 模型原理


文档可以参考https://paddlescience-docs.readthedocs.io/zh-cn/latest/zh/examples/drivaernetplusplus/
进行修改，论文的主要结论需要体现

wangguan1995 · 2025-06-03T03:52:27Z

requirements.txt

@@ -2,19 +2,25 @@ colorlog
 einops
 h5py
 hydra-core
+hydra-core


重复需要删掉

paddle-bot bot added the contributor label Mar 4, 2025

luotao1 mentioned this pull request Mar 5, 2025

【Hackathon 8th】开源贡献个人挑战赛 PaddlePaddle/Paddle#71310

Open

luotao1 added the PaddlePaddle Hackathon label Mar 5, 2025

luotao1 assigned luotao1 and wangguan1995 Mar 5, 2025

xiaoyewww force-pushed the donimo branch from b57da93 to 8cd4378 Compare March 15, 2025 15:35

wangguan1995 reviewed May 19, 2025

View reviewed changes

xiaoyewww added 4 commits May 20, 2025 23:39

feat(domino): support domino

1fb4980

feat(domino): support domino for training and test

a313b5d

feat(domino): support domino for training and test

3eea7c0

feat(domino): support domino for training and test

5d0f63e

xiaoyewww force-pushed the donimo branch from 8cd4378 to 5d0f63e Compare May 21, 2025 16:09

xiaoyewww added 2 commits May 22, 2025 22:38

feat(domino): remove something of torch

baa40d3

feat(domino): move physicsnemo to arch

f7918c7

wangguan1995 reviewed May 28, 2025

View reviewed changes

xiaoyewww added 4 commits May 29, 2025 02:00

feat(domino): refactor physicsnemo

905be9a

feat(domino): refactor physicsnemo

7074ccf

feat(domino): refactor physicsnemo

5df9b6d

feat(domino): refactor physicsnemo

385a128

wangguan1995 reviewed Jun 3, 2025

View reviewed changes

feat(domino): complement reade

2b14a8a

		@@ -0,0 +1,43 @@
		# DoMINO: Decomposable Multi-scale Iterative Neural Operator for External Aerodynamics


		## 推理

		1. 修改`conf/config.yaml`路径, 原始配置文件参考[DoMINO](https://github.com/NVIDIA/modulus/blob/main/examples/cfd/external_aerodynamics/domino/src/conf/config.yaml)。

		@@ -0,0 +1,124 @@
		# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.

		@@ -0,0 +1,109 @@
		# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.

		@@ -0,0 +1,17 @@
		# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.

		@@ -0,0 +1,18 @@
		# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.

		@@ -0,0 +1,378 @@
		# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.

		@@ -0,0 +1,1178 @@
		# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.

		@@ -0,0 +1,143 @@
		# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.

		@@ -0,0 +1,946 @@
		# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.


		外部空气动力学涉及高雷诺数Navier-Stokes方程求解，传统CFD方法计算成本高昂。神经算子通过端到端映射提升了效率，但面临多尺度耦合建模与长期预测稳定性不足的挑战。Decomposable Multi-scale Iterative Neural Operator（Domino）提出可分解多尺度架构，通过分层特征解耦、迭代残差校正及参数独立编码，显著提升跨尺度流动建模精度与泛化能力。实验显示，其计算速度较CFD快2-3个量级，分离流预测精度较FNO等模型提升约40%，为飞行器设计等工程问题提供高效解决方案。

		## 2. 模型原理

【Hackathon 8th No.13】Domino 论文复现 #1093

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【Hackathon 8th No.13】Domino 论文复现 #1093

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xiaoyewww commented Mar 4, 2025

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xiaoyewww May 22, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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wangguan1995 commented May 19, 2025

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xiaoyewww commented Mar 4, 2025 •

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xiaoyewww May 22, 2025 •

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