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Generative models such as denoising diffusion models are quickly advancing their ability to approximate highly complex data distributions.
PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 1912
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Topology Optimization
Martin P. Bendsøe and Ole Sigmund · 2004
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Denoising diffusion probabilistic models, 2020
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2006
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Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2006
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A Connection Between Score Matching and Denoising Autoencoders
Pascal Vincent · 2011
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Auto-Encoding Variational Bayes
Diederik P Kingma and Max Welling · 2013
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2014
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Sobolev Training for Neural Networks
Wojciech Marian Czarnecki, Simon Osindero, Max Jaderberg, Grzegorz Świrszcz, and Razvan Pascanu · 2017
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Stefan Elfwing, Eiji Uchibe, and Kenji Doya · 2017
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Bounds on the Jensen Gap, and Implications for Mean-Concentrated Distributions
Xiang Gao, Meera Sitharam, and Adrian E. Roitberg · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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{findiff} Software Package, 2018
Matthias Baer · 2018
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SolidsPy: 2D-Finite Element Analysis with Python, 2018
Juan Gómez and Nicolás Guarín-Zapata · 2018
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G.E. Karniadakis · 2018
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Yuxin Wu and Kaiming He · 2018
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Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification
Yinhao Zhu and Nicholas Zabaras · 2018
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Permutation invariant graph generation via score-based generative modeling
Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon · 2020
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SciPy 1.0: fundamental algorithms for scientific computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, Aditya Vijaykumar, Alessandro Pietro Bardelli, Alex Rothberg, Andreas Hilboll, Andreas Kloeckner, Anthony Scopatz, Antony Lee, Ariel Rokem, C. Nathan Woods, Chad Fulton, Charles Masson, Christian Häggström, Clark Fitzgerald, David A. Nicholson, David R. Hagen, Dmitrii V. Pasechnik, Emanuele Olivetti, Eric Martin, Eric Wieser, Fabrice Silva, Felix Lenders, Florian Wilhelm, G. Young, Gavin A. Price, Gert-Ludwig Ingold, Gregory E. Allen, Gregory R. Lee, Hervé Audren, Irvin Probst, Jörg P. Dietrich, Jacob Silterra, James T Webber, Janko Slavič, Joel Nothman, Johannes Buchner, Johannes Kulick, Johannes L. Schönberger, José Vinícius de Miranda Cardoso, Joscha Reimer, Joseph Harrington, Juan Luis Cano Rodríguez, Juan Nunez-Iglesias, Justin Kuczynski, Kevin Tritz, Martin Thoma, Matthew Newville, Matthias Kümmerer, Maximilian Bolingbroke, Michael Tartre, Mikhail Pak, Nathaniel J. Smith, Nikolai Nowaczyk, Nikolay Shebanov, Oleksandr Pavlyk, Per A. Brodtkorb, Perry Lee, Robert T. McGibbon, Roman Feldbauer, Sam Lewis, Sam Tygier, Scott Sievert, Sebastiano Vigna, Stefan Peterson, Surhud More, Tadeusz Pudlik, Takuya Oshima, Thomas J. Pingel, Thomas P. Robitaille, Thomas Spura, Thouis R. Jones, Tim Cera, Tim Leslie, Tiziano Zito, Tom Krauss, Utkarsh Upadhyay, Yaroslav O. Halchenko, and Yoshiki Vázquez-Baeza · 2020
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Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Quinn Nichol · 2021
Cited alongside, same era.
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
Han Gao, Matthew J. Zahr, and Jian Xun Wang · 2021
Cited alongside, same era.
Variational Diffusion Models
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
Cited alongside, same era.
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
Cited alongside, same era.
Conditional diffusion-based microstructure reconstruction
Christian Düreth, Paul Seibert, Dennis Rücker, Stephanie Handford, Markus Kästner, and Maik Gude · 2023
Later among the works it cites.
Aligning optimization trajectories with diffusion models for constrained design generation
Giorgio Giannone, Akash Srivastava, Ole Winther, and Faez Ahmed · 2023
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Efficient Diffusion Training via Min-SNR Weighting Strategy
Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, and Baining Guo · 2023
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Arbitrary-scale super-resolution via deep learning: A comprehensive survey
Hongying Liu, Zekun Li, Fanhua Shang, Yuanyuan Liu, Liang Wan, Wei Feng, and Radu Timofte · 2023
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Diffusion Models Beat GANs on Topology Optimization
François Mazé and Faez Ahmed · 2023
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Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery
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TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
Zhenguo Nie, Tong Lin, Haoliang Jiang, and Levent Burak Kara · 2021
Cited alongside, same era.
A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables
Maximilian Rixner and Phaedon Stelios Koutsourelakis · 2021
Cited alongside, same era.
High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
Cited alongside, same era.
Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
Sifan Wang, Yujun Teng, and Paris Perdikaris · 2021
Cited alongside, same era.
Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model
Markus J. Buehler · 2022
Cited alongside, same era.
Classifier-Free Diffusion Guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Equivariant Diffusion for Molecule Generation in 3D
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, and Max Welling · 2022
Cited alongside, same era.
Mohammed Sardar, Alex Skillen, Małgorzata J. Zimoń, Samuel Draycott, and Alistair Revell · 2023
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A physics-informed diffusion model for high-fidelity flow field reconstruction
Dule Shu, Zijie Li, and Amir Barati Farimani · 2023
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Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Denoising Diffusion Samplers
Francisco Vargas, Will Sussman Grathwohl, and Arnaud Doucet · 2023
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Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
Nikolaos N. Vlassis and WaiChing Sun · 2023
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Diffusebot: Breeding soft robots with physics-augmented generative diffusion models
Tsun-Hsuan Wang, Juntian Zheng, Pingchuan Ma, Yilun Du, Byungchul Kim, Andrew Everett Spielberg, Joshua B. Tenenbaum, Chuang Gan, and Daniela Rus · 2023
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AR-diffusion: Auto-regressive diffusion model for text generation
Tong Wu, Zhihao Fan, Xiao Liu, Hai-Tao Zheng, Yeyun Gong, yelong shen, Jian Jiao, Juntao Li, zhongyu wei, Jian Guo, Nan Duan, and Weizhu Chen · 2023
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Fast sampling of diffusion models with exponential integrator
Qinsheng Zhang and Yongxin Chen · 2023
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An optimal control perspective on diffusion-based generative modeling
Julius Berner, Lorenz Richter, and Karen Ullrich · 2024
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Constrained synthesis with projected diffusion models
Jacob K Christopher, Stephen Baek, and Ferdinando Fioretto · 2024
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Christian Jacobsen, Yilin Zhuang, and Karthik Duraisamy · 2024
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From Zero to Turbulence: Generative Modeling for 3D Flow Simulation
Marten Lienen, David Lüdke, Jan Hansen-Palmus, and Stephan Günnemann · 2024
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A diffusion model framework for unsupervised neural combinatorial optimization, 2024
Sebastian Sanokowski, Sepp Hochreiter, and Sebastian Lehner · 2024
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Diffusion Models: A Comprehensive Survey of Methods and Applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang · 2024
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Multi-Scale Reconstruction of Turbulent Rotating Flows with Generative Diffusion Models
Tianyi Li, Alessandra S. Lanotte, Michele Buzzicotti, Fabio Bonaccorso, and Luca Biferale · 2073
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