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This paper introduces an approach to endow generative diffusion processes the ability to satisfy and certify compliance with constraints and physical principles.
Exponential convergence of langevin distributions and their discrete approximations
Gareth O. Roberts and Richard L. Tweedie · 1996
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Andreas Wächter and Lorenz T Biegler · 2006
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Bayesian learning via stochastic gradient langevin dynamics
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Global convergence of langevin dynamics based algorithms for nonconvex optimization
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Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials
Sehyun Chun, Sidhartha Roy, Yen Thi Nguyen, Joseph B Choi, HS Udaykumar, and Stephen S Baek · 2020
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Homogeneous linear inequality constraints for neural network activations
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Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Aligning optimization trajectories with diffusion models for constrained design generation
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Diffusion models beat gans on topology optimization
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