A Fast AI Surrogate for Coastal Ocean Circulation Models

• URL: http://arxiv.org/abs/2410.14952v1
• Date: Sat, 19 Oct 2024 02:49:30 GMT
• Title: A Fast AI Surrogate for Coastal Ocean Circulation Models
• Authors: Zelin Xu, Jie Ren, Yupu Zhang, Jose Maria Gonzalez Ondina, Maitane Olabarrieta, Tingsong Xiao, Wenchong He, Zibo Liu, Shigang Chen, Kaleb Smith, Zhe Jiang,
• Abstract summary: Oceanographers simulate ocean current circulation along the coasts to develop early warning systems that save lives and prevent loss and damage to property from coastal hazards. Traditionally, such simulations are conducted using coastal ocean circulation models such as the Regional Ocean Modeling System (ROMS) Recent advances in deep learning and GPU architecture have enabled the development of faster AI (neural network) surrogates. This paper introduces an AI surrogate based on a 4D Swin Transformer to simulate coastal tidal wave propagation in an estuary for both hindcast and forecast (up to 12 days)
• Score: 10.800252698519634
• License:
• Abstract: Nearly 900 million people live in low-lying coastal zones around the world and bear the brunt of impacts from more frequent and severe hurricanes and storm surges. Oceanographers simulate ocean current circulation along the coasts to develop early warning systems that save lives and prevent loss and damage to property from coastal hazards. Traditionally, such simulations are conducted using coastal ocean circulation models such as the Regional Ocean Modeling System (ROMS), which usually runs on an HPC cluster with multiple CPU cores. However, the process is time-consuming and energy expensive. While coarse-grained ROMS simulations offer faster alternatives, they sacrifice detail and accuracy, particularly in complex coastal environments. Recent advances in deep learning and GPU architecture have enabled the development of faster AI (neural network) surrogates. This paper introduces an AI surrogate based on a 4D Swin Transformer to simulate coastal tidal wave propagation in an estuary for both hindcast and forecast (up to 12 days). Our approach not only accelerates simulations but also incorporates a physics-based constraint to detect and correct inaccurate results, ensuring reliability while minimizing manual intervention. We develop a fully GPU-accelerated workflow, optimizing the model training and inference pipeline on NVIDIA DGX-2 A100 GPUs. Our experiments demonstrate that our AI surrogate reduces the time cost of 12-day forecasting of traditional ROMS simulations from 9,908 seconds (on 512 CPU cores) to 22 seconds (on one A100 GPU), achieving over 450$\times$ speedup while maintaining high-quality simulation results. This work contributes to oceanographic modeling by offering a fast, accurate, and physically consistent alternative to traditional simulation models, particularly for real-time forecasting in rapid disaster response.

Related papers

• GeoFUSE: A High-Efficiency Surrogate Model for Seawater Intrusion Prediction and Uncertainty Reduction [0.10923877073891446]
  Seawater intrusion into coastal aquifers poses a significant threat to groundwater resources. We develop GeoFUSE, a novel deep-learning-based surrogate framework. We apply GeoFUSE to a 2D cross-section of the Beaver Creek tidal stream-floodplain system in Washington State.
  arXiv  Detail & Related papers  (2024-10-26T08:10:32Z)
• Super-Resolution works for coastal simulations [6.263499279406057]
  High-resolution simulations are necessary to advance understanding of many processes, specifically, to predict flooding from tsunamis and storm surges. We propose a Deep Network for Super-resolution enhancement to efficiently learn high-resolution numerical solutions. Our method shows superior super-resolution quality and fast computation compared to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-29T14:16:13Z)
• Uncertainty-enabled machine learning for emulation of regional sea-level change caused by the Antarctic Ice Sheet [0.8130739369606821]
  We build neural-network emulators of sea-level change at 27 coastal locations. We show that the neural-network emulators have an accuracy that is competitive with baseline machine learning emulators.
  arXiv  Detail & Related papers  (2024-06-21T18:27:09Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• Training neural mapping schemes for satellite altimetry with simulation data [6.591483977714996]
  Deep learning schemes have emerged as appealing solutions to address space-time problems. The scarcity of real altimetry dataset impedes the training of state-of-the-art neural schemes on real-world case-studies. Here, we leverage both simulations of ocean dynamics and satellite altimeters to train simulation-based neural mapping schemes for the sea surface height.
  arXiv  Detail & Related papers  (2023-09-19T14:32:25Z)
• AI-GOMS: Large AI-Driven Global Ocean Modeling System [3.635120568177384]
  Ocean modeling is a powerful tool for simulating the physical, chemical, and biological processes of the ocean. Here, we present AI-GOMS, a large AI-driven global ocean modeling system, for accurate and efficient global ocean daily prediction.
  arXiv  Detail & Related papers  (2023-08-06T15:59:30Z)
• Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator [57.57321628587564]
  We introduce Hybrid Graph Network Simulator (HGNS) for learning reservoir simulations of 3D subsurface fluid flows. HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure. Using an industry-standard subsurface flow dataset (SPE-10) with 1.1 million cells, we demonstrate that HGNS is able to reduce the inference time up to 18 times compared to standard subsurface simulators.
  arXiv  Detail & Related papers  (2022-06-15T17:29:57Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Learning High-Speed Flight in the Wild [101.33104268902208]
  We propose an end-to-end approach that can autonomously fly quadrotors through complex natural and man-made environments at high speeds. The key principle is to directly map noisy sensory observations to collision-free trajectories in a receding-horizon fashion. By simulating realistic sensor noise, our approach achieves zero-shot transfer from simulation to challenging real-world environments.
  arXiv  Detail & Related papers  (2021-10-11T09:43:11Z)
• Large Batch Simulation for Deep Reinforcement Learning [101.01408262583378]
  We accelerate deep reinforcement learning-based training in visually complex 3D environments by two orders of magnitude over prior work. We realize end-to-end training speeds of over 19,000 frames of experience per second on a single and up to 72,000 frames per second on a single eight- GPU machine. By combining batch simulation and performance optimizations, we demonstrate that Point navigation agents can be trained in complex 3D environments on a single GPU in 1.5 days to 97% of the accuracy of agents trained on a prior state-of-the-art system.
  arXiv  Detail & Related papers  (2021-03-12T00:22:50Z)
• Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction [88.0416857308144]
  We propose an alternative to sensor simulation, as sensor simulation is expensive and has large domain gaps. We directly simulate the outputs of the self-driving vehicle's perception and prediction system, enabling realistic motion planning testing.
  arXiv  Detail & Related papers  (2020-08-13T17:20:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.