Paper | arXiv | Poster | Tweet
Official repo for the paper Compositional Generative Inverse Design.
Tailin Wu*, Takashi Maruyama*, Long Wei*, Tao Zhang*, Yilun Du*, Gianluca Iaccarino, Jure Leskovec
ICLR 2024 spotlight.
We propose a novel formulation for inverse design as an energy optimization problem and introduce Compositional Inverse Design with Diffusion Models method(CinDM) to enable us to generalize to out-of-distribution and more complex design inputs than seen in training, which outperforms the existing works in n-body and 2D airfoil design.
Framework of CinDM:
- Install dependencies.
First, create a new environment using conda (with python >= 3.7). Then install pytorch, torch-geometric and other dependencies as follows (the repository is run with the following dependencies. Other version of torch-geometric or deepsnap may work but there is no guarentee.)
Install pytorch (replace "cu113" with appropriate cuda version. For example, cuda11.1 will use "cu111"):
pip install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudio==0.10.2+cu113 -f https://download.pytorch.org/whl/torch_stable.html
Install torch-geometric. Run the following commands:
pip install torch-scatter==2.0.9 -f https://data.pyg.org/whl/torch-1.10.2+cu113.html
pip install torch-sparse==0.6.12 -f https://data.pyg.org/whl/torch-1.10.2+cu113.html
pip install torch-geometric==1.7.2
pip install torch-cluster==1.5.9 -f https://data.pyg.org/whl/torch-1.10.2+cu113.html
Install other dependencies:
pip install -r requirements.txt
If wanting to use wandb (--wandb=True), need to set up wandb, following this link.
If wanting to run 2d mesh-based simulation, FEniCS needs to be installed:
conda install -c conda-forge fenics
All the dataset can be downloaded in this this link. Checkpoints are in this link. Both dataset.zip and checkpoint_path.zip should be decompressed to the root directory of this project.
Below we provide example commands for training the diffusion model/forward model.
An example command for training 2-body diffusion model conditioned on 0 steps to rollout 24 steps is as follows, more training setting details are in train_1d.py.
python train/train_1d.py --date_time '2023-11-20' --dataset 'nbody-2' --model_type "temporal-unet1d" --conditioned_steps 0 --rollout_steps 24 --train_num_steps 1000000 --save_and_sample_every 10000 --method_type "Diffusion" --Unet_dim 64
python traing/train_2d.py --results_folder "./checkpoint_path/diffusion_2d/"
To run experiment with FNO baseline, run:
python3 train/train_baseline.py --exp_id=naca_ellipse --date_time=2023-11-14 --dataset=naca_ellipse_lepde --n_train=-1 --time_interval=4 --save_interval=5 --algo=fno-m20-w32 --no_latent_evo=False --encoder_type=cnn-s --input_steps=4 --evolution_type=mlp-3-silu-3-silu-2 --decoder_type=cnn-tr --encoder_n_linear_layers=0 --n_conv_blocks=4 --n_latent_levs=1 --n_conv_layers_latent=3 --channel_mode=exp-16 --is_latent_flatten=True --evo_groups=1 --recons_coef=1 --consistency_coef=1 --contrastive_rel_coef=0 --hinge=0 --density_coef=0.001 --latent_noise_amp=1e-4 --normalization_type=gn --latent_size=32 --kernel_size=4 --stride=2 --padding=1 --padding_mode=zeros --act_name=silu --multi_step=1^2:0.1^3:0.1^4:0.1 --latent_multi_step=1^2^3^4 --use_grads=False --use_pos=False --is_y_diff=False --loss_type=mse --loss_type_consistency=mse --batch_size=64 --val_batch_size=64 --epochs=100 --opt=adam --weight_decay=0 --seed=0 --id=0 --verbose=1 --save_iterations=-1 --latent_loss_normalize_mode=targetindi --n_workers=20 --is_unittest=False --output_padding_str=0-1-1-0 --static_latent_size=32 --gpuid=3 --n_workers=24 --is_timing=0 --test_interval=1
python3 train/train_baseline.py --exp_id=naca_ellipse --date_time=2023-11-19 --dataset=naca_ellipse_lepde --n_train=-1 --time_interval=4 --save_interval=5 --algo=contrast --no_latent_evo=False --encoder_type=cnn-s --input_steps=4 --evolution_type=mlp-5-silu-5-silu-3 --decoder_type=cnn-tr --encoder_n_linear_layers=0 --n_conv_blocks=4 --n_latent_levs=1 --n_conv_layers_latent=3 --channel_mode=exp-16 --is_latent_flatten=True --evo_groups=1 --recons_coef=1 --consistency_coef=1 --contrastive_rel_coef=0 --hinge=0 --density_coef=0.001 --latent_noise_amp=1e-4 --normalization_type=gn --latent_size=160 --kernel_size=4 --stride=2 --padding=1 --padding_mode=zeros --act_name=silu --multi_step=1^2:0.1^3:0.1^4:0.1 --latent_multi_step=1^2^3^4 --use_grads=False --use_pos=False --is_y_diff=False --loss_type=mse --loss_type_consistency=mse --batch_size=64 --val_batch_size=64 --epochs=100 --opt=adam --weight_decay=0 --seed=0 --id=0 --verbose=1 --save_iterations=400 --latent_loss_normalize_mode=targetindi --n_workers=16 --is_unittest=False --output_padding_str=0-1-1-0 --static_encoder_type="cnn-s" --static_latent_size=32 --gpuid=2 --n_workers=30 --is_timing=0 --test_interval=1 --load_dirname "naca_ellipse_2023-11-14/" --load_filename "naca_ellipse_lepde_train_-1_algo_contrast_enc_cnn-s_evo_cnn_act_silu_hid_160_lo_mse_recef_1.0_conef_1.0_nconv_4_nlat_1_clat_3_nl_False_lf_True_reg_None_gpu:4_id_0_Hash_yAlVxifp_whdeng.p"
Here we provide commands for inverse design using the trained diffusion/forward model:
Inverse design N-body CinDM:
Trained with 2-body 24 steps, at inference, we can generalize diffusion to 8 bodies and 44 steps. More design setteing are in inverse_design_diffusion_1d.py
python inference/inverse_design_diffusion_1d.py --exp_id=new-standard-noise_sum --date_time=11-20 --n_composed=0 --compose_n_bodies=2 --design_coef="0.4" --consistency_coef="0.1" --design_guidance="standard-recurrence-10" --val_batch_size=50 --model_name="Diffusion_cond-0_rollout-24_bodies-2_more_collision" --sample_steps=1000 --compose_mode=mean-inside --design_fn_mode=L2 --initialization_mode 0
Inverse design N-body baseline:
backprop with U-Net
python inference/inverse_design_1d_baseline.py --date_time "2023-11-20_1d_baseline_Unet_NA" --method_type "Unet" --design_method "backprop" --max_design_steps 1000 --coef 1 --gamma 2 --coef_max_noise 0 --coef_grad 1 --n_composed 1 --L_bnd False --checkpoint_path "/inverse_design/checkpoint_path/Unet_cond-1_rollout-23_bodies-2.pt"
CEM with U-Net
python inference/inverse_design_1d_baseline.py --date_time "2023-11-20_CEM_Unet" --method_type "Unet" --design_method "CEM" --max_design_steps 1000 --coef 1 --gamma 2 --coef_max_noise 0 --n_composed 1 --N 1000 --Ne 100 --checkpoint_path "/project/inverse_design/checkpoint_path/Unet_cond-1_rollout-23_bodies-2.pt"
More inverse design setting details for N-body baseline in inverse_design_1d_baseline.py.
Inverse design with CinDM
python inference/inverse_design_2d.py --ForceNet_path "checkpoint_path/force_surrogate_model.pth" --diffusion_model_path "checkpoint_path/diffusion_2d/"
Inverse design with baselines
To perform inverse design with diffusion models, use python filename.py Example for two airfoils design with CEM using FNO:
python inference/baseline/inverse_design_CEM_discrete_fno_twobds.py
-
BENO (ICLR 2024): A boundary-embedded neural operator that incorporates complex boundary shape and inhomogeneous boundary values into the solving of Elliptic PDEs.
-
LAMP (ICLR 2023 spotlight): First fully DL-based surrogate model that jointly optimizes spatial resolutions to reduce computational cost and learns the evolution model, learned via reinforcement learning.
If you find our work and/or our code useful, please cite us via:
@inproceedings{wu2024compositional,
title={Compositional Generative Inverse Design},
author={Tailin Wu and Takashi Maruyama and Long Wei and Tao Zhang and Yilun Du and Gianluca Iaccarino and Jure Leskovec},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=wmX0CqFSd7}
}