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Found 15 papers, 4 papers with code
Distributed Cross-Channel Hierarchical Aggregation for Foundation Models
Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation.
ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling
It supports downscaling to 0. 9 km global resolution and processes sequences up to 4. 2 billion tokens.
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions
However, these methods struggle to produce samples with the correct proportions for each mode in multimodal distributions, especially for distributions with well separated modes.
GenAI4UQ: A Software for Inverse Uncertainty Quantification Using Conditional Generative Models
It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.
Recommendations for Comprehensive and Independent Evaluation of Machine Learning-Based Earth System Models
Given the importance of deepening our understanding and improving predictions of the Earth system on all time scales, efforts are now underway to develop forecasting models into Earth-system models (ESMs), capable of representing all components of the coupled Earth system (or their aggregated behavior) and their response to external changes.
ExoTST: Exogenous-Aware Temporal Sequence Transformer for Time Series Prediction
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes.
A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics
While these models show considerable potential, they are not ready yet for operational use in weather forecasting or climate prediction.
ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability
As the largest model of its kind, ORBIT surpasses the current climate AI foundation model size by a thousandfold.
Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation
We introduce a conditional pseudo-reversible normalizing flow for constructing surrogate models of a physical model polluted by additive noise to efficiently quantify forward and inverse uncertainty propagation.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions
Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems.
Multi-module based CVAE to predict HVCM faults in the SNS accelerator
We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs).
PI3NN: Out-of-distribution-aware prediction intervals from three neural networks
First, existing PI methods require retraining of neural networks (NNs) for every given confidence level and suffer from the crossing issue in calculating multiple PIs.
A Novel Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization
Standard ES methods with $d$-dimensional Gaussian smoothing suffer from the curse of dimensionality due to the high variance of Monte Carlo (MC) based gradient estimators.
Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques
Improving predictive understanding of Earth system variability and change requires data-model integration.
