SS研HPCフォーラム2024
2024年8月6日(火) 10:00-16:00＠富士通ソリューションスクエアS棟3階P1

生成AIで拓く
ものづくりの民主化と自動化

西口 浩司
名古屋大学 大学院工学研究科 准教授
理化学研究所 計算科学研究センター 客員研究員
nishiguchi.koji.r9@f.mail.nagoya-u.ac.jp
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西口 浩司（にしぐち こうじ）
• 専門分野：計算力学（非線形構造解析，構造-流体連成問題，高性能計算）• 経歴：―1985年：広島県広島市生まれ―2004年～2010年：広島大学 工学部（建設・環境系）と修士課程―2011年～2015年：広島大学 博士課程―2010年～2016年：日東電工株式会社 研究開発本部✓ 工場実習0.5年，CAEに関わる研究開発（3.5年），新規事業開発（２年）―2016年～2020年：理化学研究所 計算科学研究センター✓ 特別研究員，スパコン「京」PJ，スパコン「富岳」PJの研究―2020年～2023年：名古屋大学 大学院工学研究科 土木工学専攻 講師―2023年～現在：名古屋大学 大学院工学研究科 土木工学専攻 准教授✓ 客員：理化学研究所，神戸大学，山梨大学
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Acknowledgment• Supercomputer “Fugaku” research project (hp220249)―Capacity computing of elasto-plastic dynamics of component
structures for next-generation automobile design and its probabilistic
deep generative models―2022/10/01 – present―Research representative: Koji Nishiguchi, Nagoya University―Participating institutions:

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Outline
1. Background and objective
2. 3D dataset generation by supercomputer Fugaku

3. 3D generative AI incorporating structural dynamics
4. Performance verification of trained AI
5. Concluding remarks
4/34Recent studies of 3D generative AI• From 2022 onwards, not only 2D generative AI but also 3D generative AI havebeen emerging one after another.
―The number of datasets for 3D shapes is much smaller than that for natural
language and images.
―No dataset that can be applied to structural mechanics has been proposed.
Model name Release dateShap-EMay 2023

Research3D
Model architecturegroup representation
OpenAI

Data set

Number of3D data

Implicit
functionTransformer-based
diffusion model

ShapeNet（3D），WebImageText（2D）Severalmillions

3D point cloudTransformer-based
diffusion model

ShapeNet（3D），WebImageText（2D）Severalmillions

Point-E

December 2022 OpenAI

Magic3D

November 2022

NVIDIA

3D mesh

NeRF, diffusion model

COCO（2D）,ImageNet（2D）

NoneDreamFusion

September
2022

Google,UCB

Implicit
function

NeRF, diffusion model

COCO（2D）,ImageNet（2D）

None

Shap-E

Magic3D

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Lack of 3D datasets• CLIP is used to compensate for the lack of 3D datasets.
―Point-E (OpenAI, December 2022)➢https://arxiv.org/pdf/2212.08751.pdf

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Industrial impact of giga-casting
https://thelastdriverlicenseholder.com/2022/04/20/tesla-revenuegrows-81-percent-in-q1-2022/

https://lowcarb.style/2022/08/23/t
esla-gigapress-giga-texas/

Shock-absorbing structure (Tesla)

Giga-press (Tesla)• In 2020: Tesla started using gigacasting for "Model Y“ of the rear body.
―30% weight reduction, 40% manufacturing cost reduction• In 2022: Chinese BEV companies (NIO and Xpeng) and Volvo has alsodecided to adopt it.
• In 2023: Toyota Motor Corporation has also decided to adopt it.
• Difficult to find optimal structure for shock-absorbing7/34

Objective
Innovating vehicle structure with a giant aluminum die-casting

30% weight reduction
40% manufacturing cost reduction

Giga-press (Tesla)

3D generative AI (Parameter-to-3D model)
for nonlinear structural design

Magic3D （NVIDIA, 2022）

Shap-E （OpenAI， 2023）

Rapid performance improvement of 3D generative AI

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Final goal
• Automation and democratization of structural design
Text-to-parameter model(LLMs as Parameter Interpreter)

Human feedback by Non-experts
Designer

NaturalMarketer

language

HumanMechanicalfeedback

parameters

Paramter-to-3D model

3D-to-text model(LLMs to understand 3D structure)

Naturallanguage

3D structure

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Outline
1. Background and objective
2. 3D dataset generation by supercomputer Fugaku

3. 3D generative AI incorporating structural dynamics
4. Performance verification of trained AI
5. Concluding remarks
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Structure for feasibility study: crash box

https://www3.nhk.or.jp/nhkworld/en/ondemand/video/2015315/

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Many-case topology optimization• Methods to find the best distribution of material for a given domain andboundary conditions➢Linear (static, small deformation) problems: established➢Nonlinear (crash) problems: not established yet

Different topologies can be obtained by changing volume
constraints. (Yuji Wada, 2023)

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Many-case topology optimization• By changing the directionof loading, we generated
10114 cases of structural
topology.
―2,097,152 cell mesh
―16 nodes of Fugaku(64rank×8threads)―30 min for each case
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Many-case topology optimization

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Many-case crash simulation
Eulerian finite volume method*• Automatic and quick mesh generation• Robust computation for large deformation• To get shock-absorbing energy (parameter)*K. Nishiguchi, et al. "Eulerian elastoplastic simulation of vehicle structures by building-cube
method on supercomputer Fugaku." Proceedings of the International Conference on High
Performance Computing in Asia-Pacific Region. 2024.

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Many-case crash simulation• By performing an elastoplastic crash simulation on 10114 cases,
we obtained the shock-absorbing energy of each case.
―4,194,304 cell mesh
―32 nodes of Fugaku(128 rank×8 threads,1024 cores) for each―2.5 hour for each case

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Histogram of shock-absorbing energyNumber of data

Shock-absorbing energy [J]17/34

Outline
1. Background and objective
2. 3D dataset generation by supercomputer Fugaku

3. 3D generative AI incorporating structural dynamics
4. Performance verification of trained AI
5. Concluding remarks
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メッシュによる3Dデータ表現• CAEなどで広く使用され，メモリ消費は比較的少ない．
• 高品質なメッシュ生成にはノウハウや労力が必要な場合が多く，大量のデータセット作成は難しい．
• 深層学習でメッシュのグラフ構造を扱うのは容易ではない．

https://www.gradientspace.com/tutorials/2017/8/30/mesh-simplification

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ボクセルによる3Dデータ表現• 画像を認識‧生成する⽅法論 (CNN等) を活用しやすい．
• メモリ消費量が非常に大きい．
―GPUスパコンで超並列分散学習を行えば扱えないこともな
いが，現在の3D生成AIでは主流のアプローチではない．

http://www.bilderzucht.de/blog/3d-pixel-voxel/20/34

点群による3Dデータ表現• ボクセルよりメモリ消費量は少ない• 面を表現できない →界面の物理量を反映しにくい．
• 深層学習で扱うのがやや難しい．

https://laempy.github.io/pyoints/tutorials/icp.html
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Neural Fieldによる3Dデータ表現• 3D形状自体をニューラルネットワークで表現するアプローチ• メモリ消費量が少ない• 3D表現の柔軟性―任意の解像度，トポロジーが表現できる―表現できる3D情報の精細さ ∝ NN の表現力 ≒ パラメータ数• 深層学習で取り扱いやすい―3D形状を含めて，すべてニューラルネットワークで生成AIを構成できる

https://arxiv.org/pdf/1812.03828

ボクセル

点群

メッシュ

Neural Field

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Neural Fieldによる3Dデータ表現• 近年，コンピュータ・ビジョンやコンピューター グラフィックスの領域では主流の手法の一つになっている
NeRF
DeepSDFPIFu
Occupancy
Networks, IM-NET
AtlasNet
Approximating
Reflectance
Functions using
Neural Networks

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DeepSDF

Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, Steven
Lovegrove; Proceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition (CVPR), 2019, pp. 165-174• Define the SDF value (shortest distance from the surface) at
sample points in space.
• Represent a 3D shape as a vector using the coordinates ofsample points and SDF values.
SamplingpointSurface-1.5No.

SDF+2.01𝑦1𝑧1+1.023+2.0 𝑥1+1.0 𝑥2+1.5 𝑥3

𝑦2𝑦3𝑧2𝑧3

4 +2.0 𝑥4

𝑦4𝑧4+1.5

Coordinates ofsample point

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DeepSDF incorporating structural dynamics

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Positional Encoding• We introduced positional encoding to improve the reconstruction
accuracy for complex shapes.
3D shapes for training

OriginalDecoder

With positional encoding

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Outline
1. Background and objective
2. 3D dataset generation by supercomputer Fugaku

3. 3D generative AI incorporating structural dynamics
4. Performance verification of trained AI
5. Concluding remarks
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Accuracy verification of parameter-to-3D tasks
We succeeded in randomly
Input parameters
generating 3D shapes withheight）
various pattern topologies that（Absorbing energy, load direction, volume,3（12.6J,
X:0.0,
Y:0.0,
Z:-1.0,16.9cm, 15.0cm）
satisfy the input parameters.
Trained 3D AI

Energy12.7J
Accuracy（98.0%）

12.2J（98.5%）

13.1J（95.4%）

13.8J（90.6%）

13.0J（93.0%）28/34

Accuracy verification of parameter-to-3D tasks• Generated 340 shapes―17 cases with strain energy ranging from 12.6J to33.7J―Generated 20 shapes each
―Computed the strain energy of the generated shapeusing Eulerian method

Number of data

340

Average accuracy

95.7%

Median accuracy

96.5%

Average error

0.68J

Median error

0.74J

Absorbing energyof generated 3D shape[J]70% 80% 90% 100%

Input absorbing energy[J]29/34

Outline
1. Background and objective
2. 3D dataset generation by Fugaku

3. 3D generative AI incorporating structural dynamics
4. Performance verification of trained AI
5. Concluding remarks
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Summary• We have proposed 3D generative AI incorporating structuraldynamics, based on DeepSDF.
― We generated 10114 datasets of paired 3D shapes andstructural dynamics parameters using the supercomputer“Fugaku” with linear topology optimization and Eulerian method.
―We confirmed that the trained 3D generative AI can generate 3D
shapes that satisfy the input parameters with a median accuracy of96.5%.

• The proposed 3D generative AI presents new possibilities forstructural design methodologies.
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Future work
• Large-scale training of 3D generation AI―To create a dataset including millions of paired 3D shapes
and their corresponding mechanical parameters.
―Distributed parallel learning of 3D generation AI.

• To develop a 3D generative model for thin-walled structures

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AI for Manufacturing：日本の勝ち筋は？
• 生成AIを「ユーザとして使う」だけでなく「つくる側」の取り組みも急務• 生成AIの性能向上は今も続いており，未だその性能限界が見えていない．
• 「つかう側」の議論も大事だが，我が国も産官学で，生成AIを「つくる側」の取組みを，できることから今すぐ始めるべき．

• ものづくりの3D生成AIは世界的にも進んでいない• ChatGPT等はまだ序章にすぎず，現在，AGIと呼ばれる汎用的AIに加えて，各領域に特化した生成AIの研究開発が進んでいる．特に創薬・医療・マテリアル・インフォマティクスなどデータセットが豊富にある領域．
• 機械/土木/建築分野は大規模データセットが整備されていない領域であり，3D生
成AIの研究は世界的にも進んでいない．
• 日本の勝ち筋は• 機械/土木/建築分野領域では，スパコンでデータセット自体を作り出すことに加えて，競争領域と協調領域を線引きした上で，各社が協調してデータセット整備（各社ごとにデータはバラバラ）を進めることが欧米勢に勝つためには必要．
• 日本の法規制は他国に比べて生成AI開発に有利
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Final goal
• Automation and democratization of structural design
Text-to-parameter model(LLMs as Parameter Interpreter)

Human feedback by Non-experts
Designer

NaturalMarketer

language

HumanMechanicalfeedbackparameters

Paramter-to-3D model

3D-to-text model(LLMs to understand 3D structure)

Naturallanguage

3D structure

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補足資料

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Generalization performance for 3D shape• How well 3D generative AI can produce data that is similar to the true data distribution, especiallyfor unseen (test) shape.
• The trained generative AI demonstrates generalization performance for 3D shape generation.
―The 3D generation error for the test data is almost the same as the 3D generation error for thetraining data.

UnseenshapeGeneratedshape36/34