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We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of stochastic simulators.
Design and analysis of computer experiments
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Neural Networks and Analog Computation: Beyond the Turing Limit
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Agent-based simulation of a financial market
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Kriging models for global approximation in simulation-based multidisciplinary design optimization
Timothy W. Simpson, Timothy M. Mauery, John J. Korte, and Farrokh Mistree · 2001
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Metamodeling: radial basis functions, versus polynomials
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Reduction of monte-carlo simulation runs for uncertainty estimation in hydrological modelling
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Pattern recognition and machine learning
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Metamodeling using extended radial basis functions: a comparative approach
Anoop A Mullur and Achille Messac · 2006
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The numerical simulation of liquid sloshing on board spacecraft
A. E.P. Veldman, J. Gerrits, R. Luppes, J. A. Helder, and J. P.B. Vreeburg · 2006
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Church: A language for generative models
Noah D. Goodman, Vikash K. Mansinghka, Daniel Roy, Keith Bonawitz, and Joshua B. Tenenbaum · 2008
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Reduced-order nonlinear unsteady aerodynamic modeling using a surrogate-based recurrence framework
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Lightweight implementations of probabilistic programming languages via transformational compilation
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Saman Razavi, Bryan A. Tolson, and Donald H. Burn · 2012
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Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions
Khader M Hamdia, Mohammad Silani, Xiaoying Zhuang, Pengfei He, and Timon Rabczuk · 2017
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Inference compilation and universal probabilistic programming
Tuan Anh Le, Atılım Güneş Baydin, and Frank Wood · 2017
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Accelerating eulerian fluid simulation with convolutional networks
Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, and Ken Perlin · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Pyro: Deep universal probabilistic programming
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Venture: a higher-order probabilistic programming platform with programmable inference
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Recurrent neural networks as weighted language recognizers
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/Infer.NET 0.3, 2018
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An introduction to probabilistic programming
Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, and Frank Wood · 2018
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On the practical computational power of finite precision rnns for language recognition
Gail Weiss, Yoav Goldberg, and Eran Yahav · 2018
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Surrogate modeling for liquid-gas interface determination under microgravity
Zongyu Wu, Yiyong Huang, Xiaoqian Chen, Xiang Zhang, and Wen Yao · 2018
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Etalumis: Bringing probabilistic programming to scientific simulators at scale
Atilim Güneş Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip Torr, Victor Lee, Kyle Cranmer, Prabhat, and Frank Wood · 2019
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RAVEN Simulation Software
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Attention for inference compilation
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