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Found 5 papers, 1 papers with code
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators.
DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting
The improvement in DLCR's performance against the gold standard ground truth over the baseline's performance shows its potential to correct, remap, and fine-tune the mesh-gridded forecasts under the supervision of observations.
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
We present real-world use cases that range from challenging forward multi-physics simulations with turbulence and complex 3D geometries, to industrial design optimization and inverse problems that are not addressed efficiently by the traditional solvers.
ContainerStress: Autonomous Cloud-Node Scoping Framework for Big-Data ML Use Cases
Deploying big-data Machine Learning (ML) services in a cloud environment presents a challenge to the cloud vendor with respect to the cloud container configuration sizing for any given customer use case.
Turbulence Enrichment using Physics-informed Generative Adversarial Networks
We incorporate a physics-informed learning approach by a modification to the loss function to minimize the residuals of the governing equations for the generated data.
