Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
University of Oxford（牛津大学）；Intel Corporation（英特尔公司）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；CERN（欧洲核子研究组织）；University of British Columbia（不列颠哥伦比亚大学）；University of Liège（列日大学）；New York University（纽约大学）
Atılım Güneş Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe,… 展开作者
Atılım 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, Frank Wood
备注：
14 pages, 8 figures
发表信息：
Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19), November 17--22, 2019
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remain limited because of the impracticability of rewriting complex scientific simulators in a PPL, the computational cost of inference, and the lack of scalable implementations. To address these, we present a novel PPL framework that couples directly to existing scientific simulators through a cross-platform probabilistic execution protocol and provides Markov chain Monte Carlo (MCMC) and deep-learning-based inference compilation (IC) engines for tractable inference. To guide IC inference, we perform distributed training of a dynamic 3DCNN--LSTM architecture with a PyTorch-MPI-based framework on 1,024 32-core CPU nodes of the Cori supercomputer with a global minibatch size of 128k: achieving a performance of 450 Tflop/s through enhancements to PyTorch. We demonstrate a Large Hadron Collider (LHC) use-case with the C++ Sherpa simulator and achieve the largest-scale posterior inference in a Turing-complete PPL.
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
Tübingen AI Center（图宾根人工智能中心）；Machine Learning in Science, Excellence Cluster Machine Learning, University of Tübingen（图宾根大学机器学习卓越集群科学机器学习系）；Department Empirical Inference, Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所经验推断系）
We present a common framework for Bayesian emulation methodologies for multivariate-output simulators, or computer models, that employ either parametric linear models or nonparametric Gaussian processes. Novel diagnostics suitable for multivariate covariance-separable emulators are developed and techniques to improve the adequacy of an emulator are discussed and implemented. A variety of emulators are compared for a humanitarian relief simulator, modelling aid missions to Sicily after a volcanic eruption and earthquake, and a sensitivity analysis is conducted to determine the sensitivity of th…
Meta AI；UC Berkeley（加州大学伯克利分校）；École des Ponts ParisTech（巴黎桥路学院）；Univeristy of Oxford（牛津大学）
We introduce Nocturne, a new 2D driving simulator for investigating multi-agent coordination under partial observability. The focus of Nocturne is to enable research into inference and theory of mind in real-world multi-agent settings without the computational overhead of computer vision and feature extraction from images. Agents in this simulator only observe an obstructed view of the scene, mimicking human visual sensing constraints. Unlike existing benchmarks that are bottlenecked by rendering human-like observations directly using a camera input, Nocturne uses efficient intersection method…
Swiss Federal Institute of Aquatic Science and Technology, Eawag（瑞士联邦水科学与技术研究所（Eawag））
Many model based scientific and engineering methodologies, such as system identification, sensitivity analysis, optimization and control, require a large number of model evaluations. In particular, model based real-time control of urban water infrastructures and online flood alarm systems require fast prediction of the network response at different actuation and/or parameter values. General purpose urban drainage simulators are too slow for this application. Fast surrogate models, so-called emulators, provide a solution to this efficiency demand. Emulators are attractive, because they sacrific…
Computer simulations have proven a valuable tool for understanding complex phenomena across the sciences. However, the utility of simulators for modelling and forecasting purposes is often restricted by low data quality, as well as practical limits to model fidelity. In order to circumvent these difficulties, we argue that modellers must treat simulators as idealistic representations of the true data generating process, and consequently should thoughtfully consider the risk of model misspecification. In this work we revisit neural posterior estimation (NPE), a class of algorithms that enable b…
The scientific understanding of real-world processes has dramatically improved over the years through computer simulations. Such simulators represent complex mathematical models that are implemented as computer codes which are often expensive. The validity of using a particular simulator to draw accurate conclusions relies on the assumption that the computer code is correctly calibrated. This calibration procedure is often pursued under extensive experimentation and comparison with data from a real-world process. The problem is that the data collection may be so expensive that only a handful o…
Complex simulators have become a ubiquitous tool in many scientific disciplines, providing high-fidelity, implicit probabilistic models of natural and social phenomena. Unfortunately, they typically lack the tractability required for conventional statistical analysis. Approximate Bayesian computation (ABC) has emerged as a key method in simulation-based inference, wherein the true model likelihood and posterior are approximated using samples from the simulator. In this paper, we draw connections between ABC and generalized Bayesian inference (GBI). First, we re-interpret the accept/reject step…
Department of Statistics University of Washington（统计学系华盛顿大学）；Department of Computer Science Cornell University（计算机科学系康奈尔大学）；School of Mathematical Sciences and Center for Statistical Science Peking University（数学科学学院与统计科学中心北京大学）；Departments of Computer Science and Statistics Columbia University（计算机科学与统计学系哥伦比亚大学）
Mixed neural posterior estimation for simulators with discrete and continuous parameters
appliedAI Institute for Europe（appliedAI欧洲研究院）；Machine Learning in Science, University of Tübingen（图宾根大学机器学习科学系）；Tübingen AI Center（图宾根人工智能中心）；Hertie Institute for AI in Brain Health, University of Tübingen（图宾根大学脑健康人工智能研究院）；Max Planck Institute for Intelligent Systems（智能系统马克斯·普朗克研究所）；Max Planck Institute for Biological Intelligence（生物智能马克斯·普朗克研究所）
University of Oxford（牛津大学）；CERN（欧洲核子研究中心）；Lawrence Berkeley National Lab（劳伦斯伯克利国家实验室）；University of British Columbia（英属哥伦比亚大学）；University of Liege（列日大学）；Intel Corporation（英特尔公司）；New York University（纽约大学）
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which i…
