Data-driven models in fusion exhaust: AI methods and perspectives

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Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

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Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

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Here we report the progress of the development and optimization of operational scenarios for ITER and beyond, focusing upon baseline, hybrid, and steady-state scenarios since 2007. This includes advancements made by the integrated operation scenarios (IOS) ...

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The International Tokamak Physics Activity (ITPA) has developed a comprehensive overview of the results of its coordinated R&D activities in fusion physics and diagnostics implemented during the past two decades with the aim of developing an improved physi ...

Synthesizability in generative molecular design remains a pressing challenge. Existing methods to assess synthesizability include heuristics-based metrics or retrosynthesis models which predict a synthetic pathway. By contrast, an explicit approach anchors ...

A promising way to realize controlled nuclear fusion involves the use of magnetic fields to control and confine the hot plasma configuration. This approach requires superconductor magnets operating above 15 T for the next generation of fusion devices. Due ...

Data-driven models in fusion exhaust: AI methods and perspectives

Chattez avec Graph Search

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

On the added value of sequential deep learning for the upscaling of evapotranspiration

Enhancing skin lesion classification: a CNN approach with human baseline comparison

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

Neural Garment Modeling

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Integrated operation scenarios: Chapter 6 of the special issue: on the path to tokamak burning plasma operation

Transport and confinement physics Chapter 2 of the special issue: on the path to tokamak burning plasma operation

Milestone in predicting core plasma turbulence: successful multi-channel validation of the gyrokinetic code GENE

Adapting Fine-Grained Cross-View Localization to Areas Without Fine Ground Truth

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models

Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

Introduction and Overview: Chapter 1 of the Special Issue: on the Path to Tokamak Burning Plasma Operation

Directly optimizing for synthesizability in generative molecular design using retrosynthesis models

Performance of the First 80-kA HTS CICC for High-Field Application in Future Fusion Reactors