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Found 14 papers, 9 papers with code
Comprehensive Performance Modeling and System Design Insights for Foundation Models
Our analysis emphasizes the need for closer performance modeling of different transformer types keeping system features in mind and demonstrates a path towards this.
Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators
Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics.
Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators
HENS has two primary applications: (1) as a large dataset with which to study the statistics and drivers of extreme weather and (2) as a weather forecasting system.
Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction
The rapid rise of deep learning (DL) in numerical weather prediction (NWP) has led to a proliferation of models which forecast atmospheric variables with comparable or superior skill than traditional physics-based NWP.
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
To reduce the need for training data with heavy simulation costs, we mine unlabeled PDE data without simulated solutions, and we pretrain neural operators with physics-inspired reconstruction-based proxy tasks.
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning
Super-Resolution (SR) techniques aim to enhance data resolution, enabling the retrieval of finer details, and improving the overall quality and fidelity of the data representation.
Towards Stability of Autoregressive Neural Operators
Neural operators have proven to be a promising approach for modeling spatiotemporal systems in the physical sciences.
Generative Modeling of High-resolution Global Precipitation Forecasts
Forecasting global precipitation patterns and, in particular, extreme precipitation events is of critical importance to preparing for and adapting to climate change.
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe.
Adaptive Self-supervision Algorithms for Physics-informed Neural Networks
We find that training vanilla PINNs for these problems can result in up to 70% prediction error in the solution, especially in the regime of low collocation points.
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.
Quantitative in vivo imaging to enable tumor forecasting and treatment optimization
Current clinical decision-making in oncology relies on averages of large patient populations to both assess tumor status and treatment outcomes.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
A Novel Domain Adaptation Framework for Medical Image Segmentation
Our biophysics based domain adaptation achieves better results, as compared to the existing state-of-the-art GAN model used to create synthetic data for training.
