271 skills found · Page 1 of 10
HillZhang1999 / Llm Hallucination SurveyReading list of hallucination in LLMs. Check out our new survey paper: "Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models"
tessarakkt / Godot4 OceanfftTessendorf FFT based ocean waves and buoyancy in Godot 4 using compute shaders
MITgcm / MITgcmM.I.T General Circulation Model master code and documentation repository
jaychempan / Awesome LWMsA Collection of Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)
OpenDrift / OpendriftOpen source framework for ocean trajectory modelling
YuanGao-YG / NeuralOM[AAAI 2026] NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation
mom-ocean / MOM6Modular Ocean Model
ESMG / PyromsPython tools for the Regional Ocean Modeling System (ROMS)
myroms / RomsRegional Ocean Modeling System (ROMS)
hainegroup / OceanspyA Python package to facilitate ocean model data analysis and visualization.
OceanGPT / OceanGPT[沧渊] [ACL 2024] OceanGPT: A Large Language Model for Ocean Science Tasks
Stormrider31 / Storm BreakersAn epic ocean simulation (Unity 2021+ URP)
antiplasti / Plastic Detection ModelImage Recognition Model to detect plastics, glass, paper, rubbish, metal and cardboard. This is used to detect these pollution in the ocean to allow the eradication of these materials, helping marine life, fishermen, tourism and making the world resilient to climate change.
GeoscienceAustralia / Eo TidesTide modelling tools for large-scale satellite Earth observation analysis
mom-ocean / MOM5The Modular Ocean Model
doans / Underwater Acoustic Target Classification Based On Dense Convolutional Neural NetworkIn oceanic remote sensing operations, underwater acoustic target recognition is always a difficult and extremely important task of sonar systems, especially in the condition of complex sound wave propagation characteristics. Expensively learning recognition model for big data analysis is typically an obstacle for most traditional machine learning (ML) algorithms, whereas convolutional neural network (CNN), a type of deep neural network, can automatically extract features for accurate classification. In this study, we propose an approach using a dense CNN model for underwater target recognition. The network architecture is designed to cleverly re-use all former feature maps to optimize classification rate under various impaired conditions while satisfying low computational cost. In addition, instead of using time-frequency spectrogram images, the proposed scheme allows directly utilizing original audio signal in time domain as the network input data. Based on the experimental results evaluated on the real-world dataset of passive sonar, our classification model achieves the overall accuracy of 98.85$\%$ at 0 dB signal-to-noise ratio (SNR) and outperforms traditional ML techniques, as well as other state-of-the-art CNN models.
kshedstrom / RomsRegional Ocean Modeling System (with ice)
xoceanmodel / XromsWork with ROMS ocean model output with xarray
OceanBioME / OceanBioME.jl🌊 🦠 🌿 A fast and flexible modelling environment written in Julia for modelling the coupled interactions between ocean biogeochemistry, carbonate chemistry, and physics
MITgcm / XmitgcmRead MITgcm mds binary files into xarray
