Qing Wang
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This work presents a high-fidelity simulation framework for modeling large-scale wildfire scenarios that take into consideration realistic topographies, atmospheric conditions, turbulence/fire interaction, and flow dynamics. With the overall goal of enabling large-scale ensemble simulations and the integration of the simulation results into machine-learning applications, this modeling framework has been implemented into the TensorFlow programming environment. To demonstrate the capability of this simulation framework in predicting large-scale fires, we performed high-resolution simulations of a realistic wildfire scenario that is representative of the 2017 Tubbs fire.
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A TensorFlow Simulation Framework for Scientific Computing of Fluid Flows on Tensor Processing Units
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A computational fluid dynamics (CFD) simulation framework for predicting complex flows is developed on the Tensor Processing Unit (TPU) platform. The TPU architecture is featured with accelerated performance of dense matrix multiplication, large high bandwidth memory, and a fast inter-chip interconnect, which makes it appealing for scientific high performance computing (HPC). This CFD framework is implemented with the finite difference method on a collocated structured mesh, and it uses the graph-based TensorFlow as the programming library. The accuracy and speed of this framework is studied both numerically and analytically. In particular, the impact of machine precision on the mesh convergence is discussed in detail, which provides a guideline for the simulation framework to achieve the desired rate of convergence. Additionally, a linear weak scaling and a super-linear strong scaling is observed up to a full TPU v3 pod with 2048 cores. The algorithm and implementation are validated with canonical 2D and 3D Taylor Green vortex simulations. To demonstrate the capability for simulating turbulent flows, verification simulations are conducted for two configurations, namely the decaying homogeneous isotropic turbulence and a turbulent planar jet. Both simulations show good statistical agreement with reference solutions.
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Distributed Data Processing for Large-Scale Simulations on Cloud
Lily Hu
Yi-fan Chen
2021 IEEE INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY, SIGNAL & POWER INTEGRITY (2021) (to appear)
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In this work, we proposed a distributed data pipeline for large-scale simulations by using libraries and frameworks available on Cloud services. The data pipeline is designed with careful considerations for the characteristics of the simulation data. The implementation of the data pipeline is with Apache Beam and Zarr. Beam is a unified, open-source programming model for building both batch- and streaming-data parallel-processing pipelines. By using Beam, one can simply focus on the logical composition of the data processing task and bypass the low-level details of distributed computing. The orchestration of distributed processing is fully managed by the runner, in this work, Dataflow on Google Cloud. Beam separates the programming layer from the runtime layer such that the proposed pipeline can be executed across various runners. The storage format of the output tensor of the data pipeline is Zarr.
Zarr allows concurrent reading and writing, storage on a file system, and data compression before the storage. The performance of the data pipeline is analyzed with an example, of which the simulation data is obtained with an in-house developed computational fluid dynamic solver running in parallel on Tensor Processing Unit (TPU) clusters. The performance analysis demonstrates good storage and computational efficiency of the proposed data pipeline.
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Machine learning accelerated computational fluid dynamics
Ayya Alieva
Dmitrii Kochkov
Jamie Alexander Smith
Proceedings of the National Academy of Sciences USA (2021)
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Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as in weather, climate, aerodynamics and plasma physics. Fluids are well described by the Navier-Stokes equations, but solving these equations at scale remains daunting, limited by the computational cost of resolving the smallest spatiotemporal features. This leads to unfavorable trade-offs between accuracy and tractability. Here we use end-to-end deep learning to improve approximations inside computational fluid dynamics for modeling two dimensional turbulent flows. For both direct numerical simulation of turbulence and large eddy simulation, our results are as accurate as baseline solvers with 8-16x finer resolution in each spatial dimension, resulting in a 40-400x fold computational speedups. Our method remains stable during long simulations, and generalizes to forcing functions and Reynolds numbers outside of the flows where it is trained, in contrast to black box machine learning approaches. Our approach exemplifies how scientific computing can leverage machine learning and hardware accelerators to improve simulations without sacrificing accuracy or generalization.
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Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly a decade of remote-sensing data and historical fire records to predict wildfires. This prediction problem is framed as three machine learning tasks. Results are compared and analyzed for four different deep learning models to estimate wildfire likelihood. The results demonstrate that deep learning models can successfully identify areas of high fire likelihood using aggregated data about vegetation, weather, and topography with an AUC of 83%.
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