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Found 7 papers, 1 papers with code
Simultaneous emulation and downscaling with physically-consistent deep learning-based regional ocean emulators
Building on top of the success in AI-based atmospheric emulation, we propose an AI-based ocean emulation and downscaling framework focusing on the high-resolution regional ocean over Gulf of Mexico.
HAiVA: Hybrid AI-assisted Visual Analysis Framework to Study the Effects of Cloud Properties on Climate Patterns
Clouds have a significant impact on the Earth's climate system.
Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles
Our model, AiBEDO, is capable of capturing the complex, multi-timescale effects of radiation perturbations on global and regional surface climate, allowing for a substantial acceleration of the exploration of the impacts of spatially-heterogenous climate forcers.
Accelerating exploration of Marine Cloud Brightening impacts on tipping points Using an AI Implementation of Fluctuation-Dissipation Theorem
Here, we describe an AI model, named AiBEDO, that can be used to rapidly projects climate responses to forcings via a novel application of the Fluctuation-Dissipation Theorem (FDT).
Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data
With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing.
Deep Learning-Based Feature-Aware Data Modeling for Complex Physics Simulations
Data modeling and reduction for in situ is important.
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks.
